diff --git a/tests/trainer/test_optimizers.py b/tests/trainer/test_optimizers.py
index 33f0ca8ced..34112e7cbd 100644
--- a/tests/trainer/test_optimizers.py
+++ b/tests/trainer/test_optimizers.py
@@ -1,9 +1,10 @@
 import pytest
 import torch
 
-from pytorch_lightning import Trainer
+from pytorch_lightning import Trainer, Callback
 from tests.base import EvalModelTemplate
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
+from tests.base.boring_model import BoringModel
 
 
 def test_optimizer_with_scheduling(tmpdir):
@@ -298,3 +299,50 @@ def test_init_optimizers_during_testing(tmpdir):
     assert len(trainer.lr_schedulers) == 0
     assert len(trainer.optimizers) == 0
     assert len(trainer.optimizer_frequencies) == 0
+
+
+def test_multiple_optimizers_callbacks(tmpdir):
+    """
+    Tests that multiple optimizers can be used with callbacks
+    """
+    class CB(Callback):
+
+        def on_train_batch_end(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
+            pass
+
+        def on_train_epoch_start(self, trainer, pl_module):
+            pass
+
+    class TestModel(BoringModel):
+        def __init__(self):
+            super().__init__()
+            self.layer_1 = torch.nn.Linear(32, 2)
+            self.layer_2 = torch.nn.Linear(32, 2)
+
+        def training_step(self, batch, batch_idx, optimizer_idx):
+            if optimizer_idx == 0:
+                a = batch[0]
+                acc = self.layer_1(a)
+            else:
+                a = batch[0]
+                acc = self.layer_2(a)
+
+            acc = self.loss(acc, acc)
+            return acc
+
+        def configure_optimizers(self):
+            a = torch.optim.RMSprop(self.layer_1.parameters(), 1e-2)
+            b = torch.optim.RMSprop(self.layer_2.parameters(), 1e-2)
+            return a, b
+
+    model = TestModel()
+    model.training_epoch_end = None
+    trainer = Trainer(
+        callbacks=[CB()],
+        default_root_dir=tmpdir,
+        limit_train_batches=1,
+        limit_val_batches=2,
+        max_epochs=1,
+        weights_summary=None,
+    )
+    trainer.fit(model)