In the field of mobile edge computing, the k Edge Server Placement (kESP) problem has attracted extensive attention. However, existing studies have mainly focused on solving small-scale problems, and the large-scale scenarios need to be explored. Based on this observation, this paper attempts to develop a cost-effective solution for the large-scale kESP problem. The proposed algorithm named UCIS-kESP realizes the selection of a small subset from a large collection of base stations by clustering mobile users, thereby significantly reducing the search cost of the kESP problem. Subsequently, an intelligent search based on genetic algorithm is applied to find the “optimal” deployment of k edge servers from the candidate subset. The experimental analysis conducted on a public dataset demonstrates that our proposed UCIS-kESP algorithm obtains better deployment solutions than the state-of-the-art algorithms, while also exhibiting fast and stable solving efficiency. On average, our algorithm can save about 40% of the computational time. Even as the size of the kESP problem increases, the computational overhead of the UCIS-kESP algorithm grows relatively slowly.