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In order to solve the multi-objective optimization problem, this paper proposes a multi-objective cross-entropy optimization (MOCEO) algorithm based on the original single-objective cross-entropy (CE) optimization algorithm. Situations, with a low probability for optimal point, and also, locations with a high probability to fall into local optimum after tested with standard test function ZDT4 and ZDT6 problems. The algorithm is then introduced an improved method called disturbance, including recombination, variance disturbance and varying population size. Each operation contains a variable parameter. Appropriate selection of parameters can maximize the optimization ability. A set of optimal parameters is designed and the answers are verified by a comparative study with other meta-heuristic optimization algorithms such as NSGA-II, SPEA2, MOEA/D and PAES in similar conditions. The results indicate that those improvements are effective and the algorithm proposed in this paper is superior to other algorithms. It has the advantages of strong searching ability and high robustness which is applicable to challenging difficulties with unknown search spaces.