Provided is a layered global optimization method based on a DFP algorithm and differential evolution (DE). A DE algorithm has a great global detection capability and a slow later stage convergence speed, while a DFP algorithm possesses a higher local search efficiency; given this, the DFP algorithm and the DE algorithm are combined; when population fitness no longer decreases, present population gradient information can be utilized to accelerate population convergence to a global optimal point, thereby solving algorithm balance between a global detection capability and a rapid convergence capability. In an earlier stage, the method employs the DE algorithm for global detection; in a later period of the algorithm, all members in a current population are optimized through performing the DE algorithm on an upper layer and the DFP algorithm on a lower layer, thereby speeding up algorithm local convergence, and improving algorithm search efficiency.