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In expensive constrained optimization problems, the evaluation of candidate solutions could be extremely computationally and/or financially expensive. This paper proposes a method, called DyHF-GP, for reducing computation costs and raising optimization efficiency, by combining Gaussian stochastic process model(GP) with DyHF(Dynamic Hybrid Framework). In DyHF-GP, the Latin Hypercube Sampling(LHS) is used to sample points, then the true function is surrogated by GP. In evolutionary processes, the sample points and the GP are updated by retention and replacement mechanism. The using of GP and true function is controlled by error among several neighbor generations. The 13 standard test functions show that DyHF-GP has higher accuracy and retrieval efficiency. The number of FES is reduced by about 60% on average within 10 -4 error, which diminishing the computation costs of the objective functions greatly.