In this research paper, we employ D-Wave's quantum annealer to address the NP-hard problem of optimizing new metro station locations while considering both points of interest and existing stations. By formulating the problem as Quadratic Unconstrained Binary Optimization (QUBO) and Binary Quadratic Model (BQM), our approach harnesses the power of quantum computing to yield superior solutions compared to classical methods. This research has the potential to revolutionize urban transportation planning by efficiently balancing accessibility to points of interest and minimizing congestion around existing stations, offering a compelling solution to a complex urban development challenge. Notably, we emphasize the NP-hard nature of this problem, underlining its computational complexity and the significance of our quantum computing approach.