Unmanned Aerial Vehicles (UAV) are widely applied to various fields, where path planning is important to ensure the success and safety of missions. Based on the detailed analysis of the original Grasshopper Optimization Algorithm (GOA), we proposed an enhanced Mutation and Levy Grasshopper Optimization Algorithm (MLGOA). The MLGOA integrates dynamic mutation and Levy flight strategy, which improves the search ability and the quality of solutions. The mutation is dynamically adjusted to balance the global exploration and local development of the algorithm and Levy fight enhanced global search ability of the algorithm by the long tail distributed step size for random walk, and the local optimization is avoided effectively. The performance evaluation results for CEC2017 benchmark tests show that MLGOA is outperformed significantly than the original algorithm in convergence speed and optimization performance. Applying MLGOA to UAV path planning shows it more accurately finds global optima.