In this work we develop a software-in-the-loop simulator platform for Crazyflie nano quadrotor drone fleets. One of the challenges in maintaining a large fleet of drones is ensuring that the fleet performs its task as expected without collision, and this becomes more challenging as the number of drones scales, possibly into the hundreds. Software-in-the-loop simulation is an important component in verifying that drone fleets operate correctly and can significantly reduce development time. The simulator interface that we develop runs an instance of the Crazyflie flight stack firmware for each individual drone on a commercial, desktop machine along with a sensors and communication plugin on Gazebo Sim. The plugin transmits simulated sensor information to the firmware along with a socket link interface to run external scripts that would be run on a ground station during hardware deployment. The plugin simulates a radio communication delay between the drones and the ground station to test offboard control algorithms and high-level fleet commands. To validate the proposed simulator, we provide a case study of decentralized model predictive control (MPC) that is run on a ground station to command a fleet of sixteen drones to follow a specified trajectory. We first run the controller on the simulator interface to verify performance and robustness of the algorithm before deployment to a Crazyflie hardware experiment in the Georgia Tech Robotarium.