In order to solve the constrained optimization problem of objective function with time-consuming computation,a surrogate model was proposed to replace the objective function with time-consuming computation,the constraint individuals were selected based on the information of the objective function,and a surrogate-based differential evolution constrained optimization algorithm was proposed. Firstly,the Latin hypercube sampling method was used to establish the initial population,which was evaluated by the objective function with time-consuming calculation,and the neural network surrogate model of the objective function was established based on these sample data. Then,the differential evolution method was used to generate offsprings for each parent in the population,and the offspring individuals were evaluated by using the surrogate model. The feasibility rule was used to compare the offsprings with their parents and update the population,and the inferior individuals in the population were replaced with the better individuals in the alternate archive according to the replacement mechanism. Finally,the algorithm stopped when the maximum fitness evaluation number was reached,and the optimal solution was obtained. The results of this algorithm and comparison algorithms running on 10 test functions show that the results obtained by this algorithm are more accurate. The results of applying this algorithm to the I-beam optimization problem show that the number of fitness evaluations of this algorithm is reduced by 80% compared with that of the algorithm before optimization,and the number of fitness evaluations of this algorithm is reduced by 36% compared with that of FROFI (Feasibility Rule with the incorporation of Objective Function Information) algorithm. Using the proposed algorithm to realize the optimization can effectively reduce the number of calls for the objective function with time-consuming computation,improve optimization efficiency,and save computational cost.