diff --git a/CHANGELOG.md b/CHANGELOG.md
index 0f0c3298c628e8..59f75176800c0c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -4,6 +4,19 @@ All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
+## [unreleased] - YYYY-MM-DD
+
+### Added
+
+### Changed
+
+### Deprecated
+
+### Removed
+
+### Fixed
+
+- Fixed `toggle_optimizers` not handling all optimizer parameters ([#5775](https://github.com/PyTorchLightning/pytorch-lightning/pull/5775))
 
 ## [unreleased.Features] - YYYY-MM-DD
 
diff --git a/pytorch_lightning/core/lightning.py b/pytorch_lightning/core/lightning.py
index 90cd9e37c09b2c..ffb3be29d9a3d4 100644
--- a/pytorch_lightning/core/lightning.py
+++ b/pytorch_lightning/core/lightning.py
@@ -1196,22 +1196,24 @@ def toggle_optimizer(self, optimizer: Optimizer, optimizer_idx: int):
             optimizer: Current optimizer used in training_loop
             optimizer_idx: Current optimizer idx in training_loop
         """
+
+        # Iterate over all optimizer parameters to preserve their `requires_grad` information
+        # in case these are pre-defined during `configure_optimizers`
         param_requires_grad_state = {}
-        # make sure current optimizer is latest to be iterated over.
-        optimizers = [opt for opt in self.optimizers(use_pl_optimizer=False) if opt != optimizer] + [optimizer]
-        num_optimizers = len(optimizers) - 1
-        for opt_idx, opt in enumerate(optimizers):
+        for opt in self.optimizers(use_pl_optimizer=False):
             for group in opt.param_groups:
                 for param in group['params']:
-                    if num_optimizers == opt_idx:
-                        # If a param appears in 2 optimizers, revert `requires_grad` to before toggle.
-                        if param in param_requires_grad_state:
-                            param.requires_grad = param_requires_grad_state[param]
-                    else:
-                        # save requires_grad for later restoration
-                        param_requires_grad_state[param] = param.requires_grad
-                        param.requires_grad = False
-
+                    # If a param already appear in param_requires_grad_state, continue
+                    if param in param_requires_grad_state:
+                        continue
+                    param_requires_grad_state[param] = param.requires_grad
+                    param.requires_grad = False
+
+        # Then iterate over the current optimizer's parameters and set its `requires_grad`
+        # properties accordingly
+        for group in optimizer.param_groups:
+            for param in group['params']:
+                param.requires_grad = param_requires_grad_state[param]
         self._param_requires_grad_state = param_requires_grad_state
 
     def untoggle_optimizer(self, optimizer_idx: int):
diff --git a/tests/core/test_lightning_module.py b/tests/core/test_lightning_module.py
index 4d36027709900c..6fc5cf8d42933d 100644
--- a/tests/core/test_lightning_module.py
+++ b/tests/core/test_lightning_module.py
@@ -14,6 +14,7 @@
 from unittest.mock import Mock, patch
 
 import pytest
+from torch import nn
 from torch.optim import Adam, SGD
 
 from pytorch_lightning import Trainer
@@ -184,3 +185,228 @@ def optimizer_step(self, current_epoch, batch_nb, optimizer, optimizer_idx, clos
     )
 
     trainer.fit(model)
+
+
+def test_toggle_untoggle_2_optimizers_no_shared_parameters(tmpdir):
+
+    class TestModel(BoringModel):
+
+        def training_step(self, batch, batch_idx, optimizer_idx=None):
+            return super().training_step(batch, batch_idx)
+
+        def __init__(self):
+            super().__init__()
+            self.layer_1 = nn.Sequential(
+                nn.Linear(32, 32),
+                nn.ReLU(),
+                nn.Linear(32, 32),
+                nn.ReLU(),
+                nn.Linear(32, 32),
+            )
+
+            self.layer_2 = nn.Sequential(
+                nn.ReLU(),
+                nn.Linear(32, 32),
+                nn.ReLU(),
+                nn.Linear(32, 32),
+                nn.ReLU(),
+                nn.Linear(32, 2)
+            )
+
+            # set some weights to False to check untoggle works as expected.
+            self.layer_1[2].weight.requires_grad = False
+            self.layer_1[4].weight.requires_grad = False
+
+            self.layer_2[1].weight.requires_grad = False
+            self.layer_2[3].weight.requires_grad = False
+
+        def configure_optimizers(self):
+            optimizer = SGD(self.layer_1.parameters(), lr=0.1)
+            optimizer_2 = Adam(self.layer_2.parameters(), lr=0.1)
+            return [optimizer, optimizer_2]
+
+        def optimizer_step(
+            self,
+            current_epoch,
+            batch_nb,
+            optimizer,
+            optimizer_idx,
+            closure,
+            on_tpu=False,
+            using_native_amp=False,
+            using_lbfgs=False
+        ):
+            if optimizer_idx == 0:
+                assert self.layer_1[0].weight.requires_grad is True
+                assert self.layer_1[2].weight.requires_grad is False
+                assert self.layer_1[4].weight.requires_grad is False
+
+                assert self.layer_2[1].weight.requires_grad is False
+                assert self.layer_2[3].weight.requires_grad is False
+                assert self.layer_2[5].weight.requires_grad is False
+
+            if optimizer_idx == 1:
+                assert self.layer_1[0].weight.requires_grad is False
+                assert self.layer_1[2].weight.requires_grad is False
+                assert self.layer_1[4].weight.requires_grad is False
+
+                assert self.layer_2[1].weight.requires_grad is False
+                assert self.layer_2[3].weight.requires_grad is False
+                assert self.layer_2[5].weight.requires_grad is True
+
+            optimizer.step(closure=closure)
+
+    model = TestModel()
+    model.training_epoch_end = None
+
+    trainer = Trainer(
+        max_epochs=1,
+        default_root_dir=tmpdir,
+        limit_train_batches=8,
+        accumulate_grad_batches=1,
+        limit_val_batches=0,
+    )
+
+    results = trainer.fit(model)
+    assert results
+
+
+def test_toggle_untoggle_3_optimizers_shared_parameters(tmpdir):
+
+    class TestModel(BoringModel):
+
+        def __init__(self):
+            super().__init__()
+            self.layer_1 = nn.Sequential(
+                nn.Linear(32, 32),
+                nn.ReLU(),
+                nn.Linear(32, 32),
+                nn.ReLU(),
+                nn.Linear(32, 32),
+            )
+
+            self.layer_2 = nn.Sequential(
+                nn.ReLU(),
+                nn.Linear(32, 32),
+                nn.ReLU(),
+                nn.Linear(32, 32),
+                nn.ReLU(),
+                nn.Linear(32, 2)
+            )
+
+            self.layer_3 = nn.Sequential(
+                nn.ReLU(),
+                nn.Linear(32, 32),
+                nn.ReLU(),
+                nn.Linear(32, 32),
+                nn.ReLU(),
+                nn.Linear(32, 2)
+            )
+
+            # set some weights to False to check untoggle works as expected.
+            self.layer_1[2].weight.requires_grad = False
+            self.layer_1[4].weight.requires_grad = False
+
+            self.layer_2[1].weight.requires_grad = False
+            self.layer_2[3].weight.requires_grad = False
+
+            self.layer_3[1].weight.requires_grad = False
+            self.layer_3[5].weight.requires_grad = False
+
+        def optimizer_step(
+            self,
+            current_epoch,
+            batch_nb,
+            optimizer,
+            optimizer_idx,
+            closure,
+            on_tpu=False,
+            using_native_amp=False,
+            using_lbfgs=False
+        ):
+            if optimizer_idx == 0:
+                assert self.layer_1[0].weight.requires_grad is True
+                assert self.layer_1[2].weight.requires_grad is False
+                assert self.layer_1[4].weight.requires_grad is False
+
+                assert self.layer_2[1].weight.requires_grad is False
+                assert self.layer_2[3].weight.requires_grad is False
+                assert self.layer_2[5].weight.requires_grad is True
+
+                assert self.layer_3[1].weight.requires_grad is False
+                assert self.layer_3[3].weight.requires_grad is False
+                assert self.layer_3[5].weight.requires_grad is False
+
+            if optimizer_idx == 1:
+                assert self.layer_1[0].weight.requires_grad is False
+                assert self.layer_1[2].weight.requires_grad is False
+                assert self.layer_1[4].weight.requires_grad is False
+
+                assert self.layer_2[1].weight.requires_grad is False
+                assert self.layer_2[3].weight.requires_grad is False
+                assert self.layer_2[5].weight.requires_grad is True
+
+                assert self.layer_3[1].weight.requires_grad is False
+                assert self.layer_3[3].weight.requires_grad is True
+                assert self.layer_3[5].weight.requires_grad is False
+
+            if optimizer_idx == 2:
+                assert self.layer_1[0].weight.requires_grad is True
+                assert self.layer_1[2].weight.requires_grad is False
+                assert self.layer_1[4].weight.requires_grad is False
+
+                assert self.layer_2[1].weight.requires_grad is False
+                assert self.layer_2[3].weight.requires_grad is False
+                assert self.layer_2[5].weight.requires_grad is False
+
+                assert self.layer_3[1].weight.requires_grad is False
+                assert self.layer_3[3].weight.requires_grad is True
+                assert self.layer_3[5].weight.requires_grad is False
+
+            optimizer.step(closure=closure)
+
+        def training_step(self, batch, batch_idx, optimizer_idx=None):
+            return super().training_step(batch, batch_idx)
+
+        @staticmethod
+        def combine_generators(gen_1, gen_2):
+            for p in gen_1:
+                yield p
+            for p in gen_2:
+                yield p
+
+        def configure_optimizers(self):
+            optimizer_1 = SGD(
+                self.combine_generators(
+                    self.layer_1.parameters(),
+                    self.layer_2.parameters()
+                ),
+                lr=0.1
+            )
+            optimizer_2 = Adam(
+                self.combine_generators(
+                    self.layer_2.parameters(),
+                    self.layer_3.parameters()
+                ),
+                lr=0.1
+            )
+            optimizer_3 = SGD(
+                self.combine_generators(
+                    self.layer_3.parameters(),
+                    self.layer_1.parameters()
+                ),
+                lr=0.1
+            )
+            return [optimizer_1, optimizer_2, optimizer_3]
+
+    model = TestModel()
+    model.training_epoch_end = None
+
+    trainer = Trainer(
+        max_epochs=1,
+        default_root_dir=tmpdir,
+        limit_train_batches=8,
+        accumulate_grad_batches=1,
+    )
+
+    trainer.fit(model)