In recent years, remote sensing data have gradually shown a trend towards big data. The cost of the efficient storage, transmission, and processing of multi-source massive remote sensing data is very high. In practical applications, it is usually necessary to screen out data that meet specific coverage requirements from a large amount of remote sensing data, which can be regarded as a set covering problem. The traditional manual screening method is time-consuming and labor-intensive, making it difficult to meet the needs of large-scale information processing and analysis tasks across multiple fields. To improve the screening efficiency, people usually adopt the greedy algorithm for data screening, which may lead to becoming trapped in a local optimal solution. In this paper, an improved electromagnetism-like mechanism algorithm is proposed to solve the optimal screening problem of remote sensing data, using the tent chaotic map to construct the initial population, combining with a new local search strategy, and introducing the idea of differential evolutionary algorithm. The experimental results show that the improved algorithm has significant advantages in the optimal screening problem of massive remote sensing data. Compared with the greedy algorithm, the optimal solution of the improved algorithm is increased by about 9.78% on average. Compared with the original algorithm, it can search for the global optimal solution more quickly and has better robustness.