A hybrid evolutionary algorithm based on multiobjective optimization and adaptive penalty function is presented for solving constrained optimization problems. The main idea of this approach is first to take advantage of multiobjective optimization techniques to extract the main information contained in the current population, and then further selcet the most valuable informatin by using the peanlty function to direct the population to evlove. The proposed constraint-handling method is easy to implement and requires no parameter tuning. By integrating it with a model of a population-based algorithm-generator, a novel constrained optimization evolutionary algorithm is derived. Experiments on 10 benchmark test functions verify the effectiveness of the proposed method. The results show that the new approach is very robust and it achieves very competitive performance with respect to some other state-of-the-art approaches.