To address the issues of spatial distribution imbalance and low utilization rates in electric taxi charging facility siting, this paper proposes a multi-objective particle swarm optimization algorithm (FMPPSO) integrating epsilon constraint and fuzzy mathematical programming. By constructing a multi-constraint siting model incorporating land costs, passenger pickup rates, and battery degradation, we design an adaptive objective weight allocation strategy based on fuzzy membership functions to resolve the premature convergence challenge of traditional evolutionary algorithms in multi-objective optimization. An epsilon constraint mechanism introduced dynamically balances convergence and solution set diversity, generating high-quality Pareto frontier solution sets.Finally, simulation experiments and comparative analyses are conducted to validate the effectiveness of FMPPSO in solving the electric taxi charging station placement problem.