<p>Unmanned Aerial Vehicles (UAVs) are flexible autonomous systems that enable efficient data collection and task execution across diverse applications. However, their limited battery life poses a significant challenge for long-duration missions, as frequent recharging interrupts operations and reduces efficiency. This paper addresses the problem of optimizing the number and placement of charging stations in a UAV operating environment, considering the limited battery capacity of UAVs, quantified by the maximum allowable travel distance to the nearest charging station. We first present an exhaustive search method that evaluates all possible charging station configurations to ensure optimal placement while keeping the UAV’s travel distance within the predefined maximum distance. To address the exponential growth in complexity, we propose an efficient algorithm that groups areas within the operational region of the UAV system into virtual sub-areas, each containing a single charging station. It reduces computational complexity while ensuring the maximum distance constraint. The proposed algorithm aims to minimize UAV downtime and extend mission durations. To validate the efficacy of the proposed charging station algorithm, we integrate it into a reinforcement learning-based UAV target detection system designed to operate in uncertain environments. The simulation results demonstrate that the proposed algorithm significantly improves the efficiency of UAV missions, particularly in terms of detection performance, compared to a benchmark charging station algorithm. This improvement is primarily due to the strategic placement of charging stations, which optimizes the UAV’s operational capabilities.</p>