Abstract: To solve constrained multi-objective optimization problems, we propose an effective strategy for constraint handling using the degree of constraint violation and Pareto dominance, and design a novel multi-objective imperialist competitive algorithm(MOICA). We present a simple process of generating initial empires in the MOICA. We adopt a mechanism for learning colonies from non-dominated solutions in external archive in assimilation and use a new imperialist competition based on the new definition of empire power to generate the good solution. Finally, we use seven test functions CF1~CF7 to conduct experiments, and compare the MOICA with several algorithms. The computational results indicate that MOICA has a good search ability and advantages for solving constrained multi-objective optimization problems.