In the scenario of multi-user single edge computing, when a user device offloads a task, the uncertainty of the offloading scheme often leads to load imbalance, resource tension, or waste of idle resources of the mobile edge server, or even the failure of the offloading decision. To address this issue, this paper proposes a multi-user co-optimization offloading (MUCO) algorithm based on a genetic algorithm. This algorithm takes into account user energy consumption, task execution delay, and payment cost of computing resources when computing resources are limited, and adaptively selects task offloading strategies in satellite-terrestrial networks to minimize application response costs. The simulation experiment results show that the proposed MUCO algorithm has good convergence, lower response costs, and good adaptability to different computing resource prices compared to only local computation and only full MEC offloading.