Autonomous vehicles are playing an increasingly im- portant role in industry, particularly for tasks such as exploration and inspection. Combining different types of autonomous vehicles such as Autonomous Underwater Vehicles (AUVs) and Unmanned Aerial Vehicles (UAVs) can enhance operational efficiency and accelerate mission completion. This paper introduces a novel approach to cooperative mission planning, demonstrated through a simulation-based case study in a harbor environment. We designed and evaluated various swarm formations using the OMNeT++ simulation framework to assess the execution of inspection missions, with the target to detect pollution in the water. The evaluation compares multiple swarm configurations, including two AUVs, three AUVs in a triangular formation, and a hybrid AUV-UAV swarm. For baseline comparison, missions using individual AUVs and UAVs were also analyzed. The results show that the hybrid swarm achieves the fastest detection and response times, emphasizing the advantages of cooperative mission planning across heterogeneous autonomous systems.