Conventional engineering design optimization requires a large amount of expensive experimental tests from prototypes or computer simulations, which may result in an inefficient and unaffordable design process. In order to overcome these disadvantages, a surrogate model may be used to replace the prototype tests. To construct a surrogate model of sufficient accuracy from limited number of tests/simulations, a multi-level surrogate modeling strategy is introduced in this paper. First, a chosen number of points determined by Optimal Latin Hypercube (OLH) Design of Experiments are used to generate global-level surrogate models with Genetic Programming and the fitness landscape can be explored by Genetic Algorithms for near-optimal solutions. Local-level surrogate models are constructed then from the extended-OLH samples in the vicinity of global optimum on the basis of a much smaller number of chosen points. As a result, an improved optimal design is achieved. The efficiency of this strategy is demonstrated by the parametric optimization design of a Piezoelectric Flex Transducer (PFT) energy harvester. The optimal design is verified by finite element simulations and the results show that the proposed multi-level surrogate modeling strategy has the advantages of faster convergence and more efficiency in comparison with the conventional single level surrogate modeling technique.