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Multi-objective evolutionary algorithms have attracted more and more attentions because their powerful solving ability of dealing with several conflict objective functions for a multi-objective optimization problem. As a novel evolutionary algorithm, extremal optimization has been applied successfully to a variety of discrete, continuous optimization test functions and engineering optimization problems, but there is limited research works concerning multi-objective extremal optimization. This paper introduces the basic ideas of Pareto optimization to extremal optimization, and proposes a multi-objective extremal optimization algorithm with multi-non-uniform mutation for solving the continuous multi-objective optimization problems. The simulation experiments on six well- known continuous multi-objective optimization test functions have demonstrated that the proposed algorithm in this paper is superior to other traditional multi-objective optimization algorithms such as NSGA-II, PAES, SPEA and SPEA2 in terms of convergence and diversity.