The integration of unmanned aerial vehicles (UAVs) as auxiliary devices in mobile edge computing (MEC) has become increasingly common. This paper proposes a UAV-assisted edge computing deployment strategy that utilizes the mobility of UAVs for realistic Internet of Things (IoT) scenarios with large-scale signal fading. By applying the law of cosines, the continuous variation of distance between moving UAVs and ground devices is formulated. Based on these distance variations, an optimization strategy for both UAV positioning and task offloading is developed. The primary objective is to minimize the total task execution time. A mathematical model of the optimization problem is established and solved using a hybrid approach that combines genetic algorithms with deep reinforcement learning. Finally, simulation results are presented to verify the effectiveness of the proposed algorithm and strategy.