With the advent of big data era, there is a growing demand for users to store data in the cloud. To enhance the availability and privacy of data in the cloud and mitigate risks such as vendor lock-in, distributed multi-cloud storage has attracted widespread attention. However, accessing data from the cloud often encounters issues such as high latency and significant bandwidth costs. Utilizing edge resources can effectively address these problems, but it may potentially reduce data availability and increase storage costs. To this end, we combine multi-cloud and edge resources in this work, taking into account access restrictions on edge resources, and construct a multi-cloud edge storage model. Then, an Opposition-Based Learning Binary Battle Royale Optimizer (OBL-BinBRO) strategy is proposed to find optimal data placement solution, which assists users in determining the cloud and edge service providers for storing and accessing data. Using real-world data for extensive experiments, compared to several representative data placement strategies, the proposed method can effectively reduce the total cost of data placement for users.