A multiple-searching strategies guided ant colony optimization algorithm (MSACO) is proposed to address the problem of insufficient global search capability and susceptibility to local optima in the 3D trajectory planning process of UAVs using classical ant colonies. The algorithm first combines the improved artificial potential field method to create a guiding zone to enhance the initialization pheromone distribution strategy, providing regional reference for the entire optimization process of the ant colony and improving its global search ability. Then, relying on multiple neighborhood inertial search strategies and new pheromone calculation methods, the dynamic expansion of ant colony optimization step size is achieved, reducing the number of path turning points and nodes, and enhancing the balance and smoothness of the optimal path. Subsequently, the heuristic function optimization strategy is used to dynamically adjust the heuristic information adjustment factor in each stage of ant colony optimization, improving the algorithm’s self-learning ability, adaptability, and convergence efficiency. In the experiment, through horizontal comparison of test functions and vertical application in complex 3D task scenarios, the ant colony optimization algorithm guided by multiple search strategies has improved its drone trajectory planning ability compared to the classical ant colony algorithm in the new objective function.