Aiming at multi-objective optimization problems with interval parameters, a multi-objective particle swarm optimization algorithm based on fuzzy dominance is proposed. Firstly, the fuzzy Pareto dominance relation based on decision-makers’ pessimism degree is defined for comparison of solutions. Then, the crowding distance suitable for interval objectives is defined for updating the external repository and selecting the global particle leaders. Finally, trials are carried out on several interval multi-objective benchmark testing functions, and the results show the effectiveness of the proposed algorithms.