This paper addresses the challenges posed by computational resource limitations and energy consumption in drone-assisted edge computing. We begin by developing a model for drone-assisted airborne users involved in computational task offloading. This model considers both resource allocation among drones and trajectory deployment, with the aim of minimizing energy cost during task completion. To achieve this, we propose a solution using Particle Swarm Optimization (PSO) to optimize both drone flight trajectories and computational resource allocation. Through simulations, our method demonstrates superior energy efficiency and task completion rates compared to existing strategies. We achieve full area coverage and effectively minimize energy consumption for the assisted drone by dynamically optimizing resource distribution.