Electric vehicles (EVs) play a significant role in enhancing the sustainability of transportation systems. However, their widespread adoption is hindered by inadequate public charging infrastructure, particularly to support long-distance travel. Identifying optimal charging station locations in large transportation networks presents a well-known NP-hard combinatorial optimization problem, as the search space grows exponentially with the number of potential charging station locations. This paper introduces a quantum search-based optimization algorithm designed to enhance the efficiency of solving this NP-hard problem for transportation networks. By leveraging quantum parallelism, amplitude amplification, and quantum phase estimation as a subroutine, the optimal solution is identified with a quadratic improvement in complexity compared to classical exact methods, such as branch and bound. The detailed design and complexity of a resource-efficient quantum circuit are discussed.