In simulation model based engineering design optimization, adopting high-fidelity and high-cost analysis model may cause unaffordable computational cost while adopting low-fidelity and low-cost analysis model may lead to optimization results with low reliability, which is difficult to meet the needs of engineering. In order to balance the contradiction between high accuracy and low cost,a sequential hierarchical Kriging model was established to fuse high/low fidelity data. In the proposed approach, many low-cost and low-fidelity sample points were used to indicate the changing trends of the high-fidelity analysis model, and a small number of high-cost and high-fidelity sample points were used to calibrate the low fidelity analysis model, so as to achieve high-fidelity prediction of the optimization objectives. To avoid the influence of hierarchical Kriging model errors on the optimization results, the hierarchical Kriging model was combined with the genetic algorithm to calculate the prediction interval of each generation of optimal solution according to 6σ criteria. The current optimal solution with a large prediction interval would be selected as a new high fidelity sample point. At the same time, sequentially updating the hierarchical Kriging model in the optimization process could improve the prediction accuracy of the hierarchical Kriging model near the optimal solution, so as to ensure the reliability of design results. The proposed approach was applied to the design optimization of a micro-aerial vehicle fuselage structure to verify its effectiveness and superiority. The grid models with different number of elements were selected as the low-fidelity analysis model and high-fidelity analysis model, respectively. Sixty low-fidelity sample points and twenty high-fidelity sample points were selected by optimal Latin hypercube design (OLHD) to construct the initial hierarchical Kriging model. The design optimization problem was solved by the proposed approach, and the solution results were compared with the results solved by the high-fidelity simulation model. The results showed that the proposed approach could effectively utilize the information of high/low fidelity data to construct hierarchical Kriging model with high accuracy and only a small amount of computational cost was required to obtain the approximate optimal solution. The proposed approach can effectively improve the design efficiency and provide a reference for similar structure design optimization problems.