An effective mission planning method can optimize the execution sequence of an unmanned aerial vehicle (UAV) and improve mission execution efficiency. Existing mission planning algorithms are mostly designed for static environments and cannot effectively handle real-time missions added in dynamic environments. This paper proposes a new mission planning method based on the estimated flight trajectory of a UAV. It uses a genetic algorithm for initial global mission planning, and when new missions arise, it replans the missions in local areas according to an adaptive local adjustment strategy. During the replanning process, issues such as UAV loss of control due to drastic changes in local trajectories may occur. To address this, a Bezier curve is used to combine the reference trajectory and the corrected trajectory for optimization. The proposed method is compared with global replanning, greedy algorithm-based mission planning, and Euclidean distance-based mission planning methods in experiments. The results demonstrate significant performance improvements.