A new multi-objective particle swarm optimization (MOPSO) technique for environmental/economic dispatch (EED) is proposed. Infeasible solutions can be revised to feasible ones by designing specific constraints correction factor. And on the basis of that, a new fitness function model for multi-objective particle swarm is built based on the penalty function method. The historical set and global set for non-dominated solutions are formed, according to the Pareto dominant conditions. A crowding distance-based approach is introduced to assign the global leader. Moreover, a new technique called slope method is proposed to further filter the non-dominated solutions based on the slope characteristics of the Pareto optimal front (POF). Then, fuzzy mathematical method for satisfaction evaluation is employed to extract the best compromise solution over the POF. Finally, several optimization runs of the proposed algorithm are carried out on the standard IEEE 30-bus test system, the results validate that the proposed method is feasible and effective. This work is supported by National Natural Science Foundation of China (No. 51207113), Research Fund for the Doctoral Program of Higher Education of China (No. 20110141110032), and State Key Laboratory of Electrical Insulation and Power Equipment of Xi'an Jiaotong University (No. EIPE13205).