In this paper, a parallel sampling strategy dedicated to SAO is proposed to enhance the competence of exploration and convergence simultaneously in the optimization process. In the parallel sampling procedure, new sampling points are identified in the pareto front of the multi-objective optimization problem, which is solved with the Normal constraint (NC) method. The objectives of the optimization problem are the two indices to represent the competence of exploration and convergence, and the sampled points will be evaluated by the true model in a parallel way. Furthermore, the proposed methodology is evaluated on two benchmark tests. Compared to other optimization algorithms, the PSAO algorithm yields equivalent or better objective values while the number of optimization iterations required to find the same global optima is reduced by multiple orders of magnitude, which substantially reduce the computing costs in the tested structural design optimization tasks, highlighting the applicability of the PSAO structural design optimization problems.