Two goals of multi-objective evolutionary algorithms are effffectively improving its convergence and diversity, and making the Pareto set evenly distributed and close to the real Pareto Front. This paper proposes a grey wolf optimization based self-organizing fuzzy multi-objective evolutionary algorithm. Grey wolf optimization algorithm is used to optimize the initial weights of the self-organizing map network. New neighborhood relationships for individuals are built by self-organizing map, which can maintain the invariance of feature distribution and map the structural information of the current population into Pareto Sets. Based on this neighborhood relationship, this paper uses the fuzzy differential evolution operator, which constructs a fuzzy inference system to dynamically adjust the weighting parameter in the difffferential operator, to generate a new initial solution, and the polynomial mutation operator to refifine them. Boundary processing is then conducted. Experiments on fififteen test problems were conducted to verify its effffectiveness. Results show that the convergence and diversity of the proposed algorithm are better than several state-of-the-art multi-objective evolutionary algorithms.