A novel constrained optimization evolutionary algorithm is proposed for solving constrained optimization problem, which does not introduce penalty parameters to deal with constraints. In the process of opulation evolution, the proposed algorithm searches the solution space of the problem through three different crossover methods based on population feasibility. A mixed mutation strategy is used to guide the process fast toward the feasible region of the search space. In addition, an infeasible solution diversity conservation and replacement strategy is used to keep a certain number of infeasible
solutions in each generation so as to enforce the evolutionary search toward an optimal solution from both sides of feasible and infeasible regions. The proposed algorithm is tested on eight well-known constrained optimization problems, and the experiment result shows the effectiveness and feasibility of the method.