In computation-intensive and latency-sensitive tasks, unmanned aerial vehicle (UAV)-assisted mobile edge computing has been extensively studied due to its high mobility and low deployment costs. However, the energy consumption of UAVs limits their ability to work for extended periods, and there are often dependencies among different modules within offloading tasks. To address these issues, directed acyclic graph (DAG) is utilized to model the dependencies among internal modules of tasks. Considering the impacts of system latency and energy consumption, an optimal offloading strategy is derived to minimize system costs. To achieve optimization, a binary grey wolf optimization algorithm based on subpopulation, Gaussian mutation, and reverse learning (BGWOSGR) is proposed. Simulation results show that the proposed algorithm reduces system costs by around 19%, 27%, 16%, and 13% compared to four other methods, with a faster convergence speed.