To address the path planning problem of inspection UAVs in complex mountainous environments, a PID search algorithm based on a hybrid genetic algorithm in a spherical coordinate system, termed HPSA, was proposed. The global exploration capability and the ability to escape local optima were enhanced by introducing a weighted elite-guided mechanism and integrating the crossover operation from genetic algorithms. The generated path was smoothed using B-spline curves to make the planned trajectory more suitable for UAV flight. Comparative experiments were conducted between HPSA and five other algorithms on the CEC2017 benchmark function set and in multi-UAV path planning scenarios with varying complexities. The experimental results indicated that HPSA exhibits superior optimization performance on the CEC2017 test set. In multi UAV path planning tasks, flight paths with the minimum total cost function are effectively obtained, thereby improving the path planning capability of multiple UAVs in complex mountainous environments.