Abstract: Power wireless networks have the advantages of high reliability and security, but there are many unfavorable factors such as limited frequency band resources and difficulty in energy extraction of base stations in power transmission and transformation scenarios. As a centralized solution, cloud computing can provide sufficient computing resources, but power Internet of Things (IoT) devices often have the problems of low bandwidth and high latency when communicating with cloud servers. Therefore, the researchers put forward the concept of edge computing, which combines the advantages of cloud computing and edge computing, and cloud-edge collaboration is gradually widely used in a complementary operation mode. In this paper, an improved optimization algorithm for computing task placement in the cloud-edge collaboration scenario is proposed. The computing task placement algorithm based on memetic algorithm (MA), to minimize the energy consumption of power IoT devices and the execution time of power IoT applications. The MA-based computing task placement algorithm is divided into three stages: the pre-scheduling phase, the computing task placement phase of parallel applications, and the fault recovery phase. Through the simulation results, compared with the existing algorithms, the performance of the proposed algorithm in this paper is significantly improved, including bandwidth, maximum number of iterations, decision time.