Abstract: To address the limitations of traditional configuration methods that rely on static indicators or linearized models—resulting in insufficient adaptability and conservative outcomes—a hybrid genetic algorithm-based optimization method for distributed synchronous condenser configuration in renewable energy power stations is proposed. The approach first screens candidate nodes through voltage sensitivity and short-circuit capacity analysis to narrow the optimization space. A comprehensive optimization model is established with economic efficiency and voltage stability as core objectives. A hybrid genetic algorithm incorporating simulated annealing is designed to solve the problem, yielding optimized configuration results. Experimental results demonstrate that the proposed method achieves rapid fitness value improvement, swiftly locating high-performance solution regions. The monitoring data shows high short-circuit ratios at bus nodes and significant margin for critical short-circuit ratios, validating the method′s remarkable advantages in enhancing system robustness and ensuring grid safety.