Limited time for multi-UAV emergency power inspection requires prioritizing targets based on fault probabilities. In order to improve the inspection efficiency, multi-charging stations can be introduced to reduce the impact of insufficient endurance of UAVs. The problem is formulated as a Multi-depot Multi-visit Team Orienteering Problem and addressed with a Genetic Algorithm with a Soft Actor-Critic model. The algorithm first incorporates two types of local search operators into the evolution process of the traditional genetic algorithm to optimize the selection of multiple UAVs visiting targets and to reduce the flight path distance of the UAVs. Secondly, a method for dynamically adjusting the parameters of genetic algorithm using reinforcement learning is proposed. By using the SAC model based on maximum entropy policy learning, during the iteration of genetic algorithm, dynamically adjusts crossover, mutation rates, and weight distance ratios in chromosome reinsertion based on past learning and population state. Experiments show the algorithm"s effectiveness in small and large-scale tests, with ablation experiments validating the local search operator"s effectiveness and the superiority of parameter tuning method. Finally, the algorithm’s efficacy in various emergency scenarios was validated via simulations.