Summary: By using differential evolution algorithm to solve multiobjective optimization problems, chaotic differential evolution for multiobjective optimization (CDEMO) is proposed. Chaotic sequences are used in the initialization of the evolutionary population and chaotic population candidate is created with chaotic variables to be used in substitution operation. The operation not only helps to maintain uniformity of the Pareto optimal solution set, but enhances the algorithm’s searching ability. The optimization performance of CDEMO is evaluated and the effectiveness of the proposed method is shown by numerical experimental results.