To solve the problem of slow convergence speed before reaching the global optimum and low precision of optimization in Grey Wolf Optimizor (GWO), a hybrid GWO algorithm based on fuzzy weight strategy is proposed. By replacing the linear convergence factor in original algorithm with a new non-linear convergence factor, global search ability is improved. Furthermore, the algorithm employs a fuzzy weight strategy to offer discrepant weight to agents who are responsible for the decision, which will enhance the optimizing ability therefore. Numberical experiments are conducted in 23 standard test functions. Experimental results show that the proposed FWGWO algorithm has better performance compared with other algorithms.