A ε-dominance based multi-objective self-organizing migrating algorithm(EMSOMA) is proposed. Firstly, a hybrid migrating behavior based self-organizing migrating algorithm(HBSOMA) is improved to quickly search the Pareto optimal solutions of the multi-objective optimization problems. The improved HBSOMA introduces the idea of comprehensive learning into the migration process, in which each component of individual can learn from different individuals for exchanging information adequately. Moreover, a learning factor is employed in the migration process to extend the migrating direction and change the step size flexibly. And based on the ε-dominance relationship, EMSOMA can maintain the diversity of the population. Finally, the experiment analysis shows the effectiveness of the proposed method.