The remote sensing data optimization screening problem is to select fewer image data of superior quality under the constraint of maximizing the coverage of the target region and can be viewed as a set covering problem (SCP) [1; 4]. The SCP is a classical NP-complete problem, and to date, all NP-complete problems have not been solved in polynomial algorithmic time [5]. The SCP has been extensively explored in the fields of optimization and mathematical planning, with initial work relying on the proper use of branch-and-bound and branch-and-cut algorithms, exact methods which are often unable to deal with large instances of the SCP [6]. There are also those who have proposed the greedy algorithm. However, because it adheres to fixed and predetermined rules, it may be limited in the process of searching for the optimal solution and is likely to be trapped in local optimal solutions. The SCP is widely applied in various fields, including facility location [7; 8; 9], base station deployment in wireless communication networks [10], and crew scheduling for railway and public transportation companies [11]. With the development of computer technology and intelligent algorithms, many scholars began to try to apply intelligent optimization algorithms to a wide variety of optimization problems. For example, ant colony optimization [12], tabu search [13], simulated annealing [14], and the genetic algorithm [15] have been widely proposed to solve the classical SCP. Recent metaheuristics for solving this problem can also be found in the literature, e.g., firefly optimization [6; 16], cat swarm optimization [17], the shuffled frog leaping algorithm [18; 19], and the electromagnetism-like mechanism algorithm [11], etc. These algorithms have demonstrated unique advantages and potential in solving various optimization problems such as the SCP. Each algorithm draws inspiration from different natural phenomena or biological behaviors, providing diverse ideas for solving complex problems. In this paper, the EM algorithm is chosen to solve the optimal screening problem of remote sensing data. The Electromagnetism-like mechanism (EM) algorithm is an intelligent optimization algorithm proposed by [PERSON] et al. in 2003 [20]. Based on the principles of electromagnetism, it regards each solution as a charged particle and conducts the search according to the action of electromagnetic forces. The electromagnetic forces not only guide the particles to approach the currently known better solutions but also subject the particles to the combined influence of the charges of other particles, forming a dynamic and multi-directional search force. This search method enables the EM algorithm to explore and balance more effectively among different local optimal regions during the optimization process. It does not simply gather towards a single global optimal direction or a local optimal region. The EM algorithm features a simple structure, a fast convergence speed, easy implementation, and a small number of parameters to be adjusted. It can not only be used as an independent optimization algorithm to solve problems but also easily absorb the advantages of other algorithms and be combined with other algorithms to form new optimization algorithms [21]. The innovations of this paper regarding the EM algorithm are as follows: (1) The initial population is generated by using the tent chaotic map, which is a chaotic map model based on segmented linear functions, and is named because its function image resembles a tent. (2) A new local search strategy is combined and an additional heuristic operator is employed to generate feasible solutions. A heuristic feasibility operator is some operation or rule designed to guide the search process according to the particular situation of the problem. (3) The idea of the differential evolutionary (DE) algorithm is introduced. The differential evolutionary algorithm is an evolutionary algorithm based on population differences.