The invention relates to a sequence radial basis function agent model-based high-efficiency global optimization method, and belongs to the technical field of multidisciplinary optimization in engineering design. The method comprises the following steps of: according to initial conditions given by a user, selecting sample points in a primary iteration design space, calculating a response value of a true model, constructing a radial basis function agent model, calculating the current optimal solution of the radial basis function (RBF) agent model, calculating a response value of the possible optimal solution of the current iteration in the true model, judging whether the global optimization method meets the convergence criterion, determining an important sampling space of the next iteration, increasing new sample points in the constructed important sampling space by an experimental design calculation method, saving the new sample points in a design sample point database and making k equal to k+1, and switching to the constructed radial basis function agent model for the next iteration. Through the method, the true models in the engineering design and analysis software are approximated, and the optimization design of the true models only takes several or dozens of seconds, so the period of the engineering optimization design is greatly shortened, the design cost is greatly saved and the efficiency is obviously improved.