The P-V curve of photovoltaic system is multimodal due to the effect of partial shading. This reduces the tracking efficiency of the traditional maximum power point tracking algorithm. To handle this effect, this paper proposes a two-layered maximum power point tracking algorithm. The differential evolution algorithm is placed in the slave layer, whereas the gray wolf optimization algorithm is placed in the master layer. In order to search the optimal duty cycle that maximizes the power output of photovoltaic system, the methods of replacement and feedback are employed to strengthen the cooperation between two algorithms. Firstly, the duty cycle is considered as the individual and the gray wolf of each algorithm, respectively. Then, differential evolution algorithm is used to search multiple groups of individuals rapidly, and the positions of the wolves in master layer are replaced by the best duty cycle in each group. Finally, the grey wolf optimization algorithm is employed to optimize the positions of wolves, and the α wolf is feed back to the slave layer. This can guide the update of individuals in the slave layer. With the platform of Matlab2017a/Simulink, the proposed algorithm is applied to simulate four cases under different magnitudes of shading. The results indicate that the efficiencies of proposed algorithm are 99.63%, 99.91%, 99.41%, and 99.95% in four cases, respectively. All these efficiencies are above those of other three existing algorithms. The energy production of photovoltaic system can be well improved by the proposed algorithm.