<p>Existing optimization algorithms struggle with complex problems with various characteristics, exhibiting subpar convergence, inadequate distribution of results, etc. Addressing these limitations, this paper presents an evolutionary algorithm with an enhanced clustering genetic strategy (MOEA-CGS). Within this algorithm, the clustering fitness indicator selection strategy (CFIS) constructs a mating pool that balances diversity and convergence using both clustering algorithms and a novel fitness indicator. Furthermore, a multi-strategy mating approach amalgamates the strengths of three genetic manipulation methods thereby boosting the algorithm’s search efficiency and adaptability across a broader spectrum of multi-objective optimization problems. By integrating a new population initialization method and population immunity operation, the algorithm enhances its capacity for space exploration while mitigating the risk of local optimum. Evaluation against 16 benchmark problems and comparison with famous algorithms demonstrate superior overall performance. Moreover, applying the algorithm to double-layer spraying trajectory planning reveals notable improvements in paint surface evenness and efficiency.</p>