In order to combine constraints into the evaluation of feasible solutions, a set of interior penalty rules is proposed to improve the efficiency of evolutionary algorithms in solving the constrained optimization problems. In these rules, interior penalty functions are used to evaluate feasible solutions and constraint violations are used to evaluate infeasible solutions. In addition, an interior penalty rule based evolution strategy algorithm is derived to solve constrained optimization problems. The theory validity of these rules is analyzed based on the successful rate of an (1+1) evolutionary algorithm. The proposed approach is tested with 13 benchmark problems. The results indicate that the presented approach is competitive with two existing state-of-the-art techniques.