This paper presents research focused on enhancing the efficiency of flight path planning for fixed-wing UAVs. The core path planning algorithm operates on a continuous search space and proceeds in two main stages. First, an intelligent random tree method is used to generate a set of feasible flight paths. Then, im-proved genetic algorithm operators are applied to identify the optimal path from this set. To accelerate the planning process, a parallel computing model was ex-plored and implemented. Simulation results show that the algorithm successfully generates an optimal flight path, and the initialization speed of feasible flight paths increases by approximately 3.5 times when using eight parallel processes.