Autonomous maritime search and rescue faces critical challenges due to limited endurance and dynamic victim drift. This thesis presents a centralized multi-UAV coordination framework based on Energy-Aware Informative Path Planning. The aim is to maximize mission endurance by exploiting the distinct energy dynamics of different flight regimes, such as the efficiency of fixed-wing cruise versus the high cost of hovering. Locally, a Receding Horizon Planner optimizes the trade-off between entropy reduction and energy consumption. The framework provides global coordination by dynamically switching agents between high-altitude exploration and low-altitude target tracking while accounting for the effects of ocean drift. Monte Carlo simulations using the empirical power model of the Variable Skew Quad Plane evaluate the swarm size required to achieve 80% global entropy reduction and successfully confirm multiple victims within a strict 10-minute timespan. The evaluation reveals a fundamental trade-off between spatial clearing speed and environmental robustness—defined as the system's ability to maintain consistent search performance despite adverse wind disturbances. Although boustrophedon coverage is efficient in optimal conditions, adverse crosswinds cause accelerated energy drain, potentially leading to early mission failure. By dynamically surfing wind gradients, the proposed method extends mission endurance while maintaining sufficient coverage capabilities in these severe crosswind scenarios. Finally, the communication and coordination logic of the framework was validated through real-world flight tests with a swarm of Parrot Bebop 2s.