UAV swarms have gained popularity over the past years, with increased accessibility contributing to new research areas and applications. The increased complexity of handling of UAVs swarms and assigning multi-phase applications results in the need for efficient task assignment for UAV swarms. The project extends an existing UAV swarm simulator to enable comparison of scheduling algorithms and techniques. The simulator is easily augmentable and provides a simple-to-use interface for the specification of complex applications. The evaluation of five scheduling algorithms shows that existing solvers for global problem optimization outperform naive scheduling approaches in terms of performance and assignment quality. Adaptive task batching and the utilization of cloud resources can lead to better task assignments with lower overhead and reduce the computational burden on the swarm.