This study presents a mixed-integer nonlinear programming (MINLP) framework to optimize the allocation of electric vehicle charging stations (EVCSs) in existing indoor parking facilities. The model minimizes total life-cycle cost by jointly determining charger types and placements while accounting for spatial feasibility and investment constraints. A hybrid search method that combines complete enumeration with dynamic programming is applied to identify the least-cost configuration within geometric and electrical limitations. The results show that configurations combining dual- and quad-port chargers outperform single-port layouts by reducing redundant electrical and installation costs. The analysis confirms that integrating life-cycle costing with spatial feasibility yields a practical decision-support tool for property owners seeking to expand charging capacity within existing facilities. Overall, the framework demonstrates that cost-efficient retrofitting of EV charging infrastructure can be achieved without additional land development, supporting broader sustainability objectives and promoting low-carbon mobility. Future research will extend the model to multiple facility layouts and incorporate sensitivity and uncertainty analyses to evaluate robustness under varying geometric and economic conditions. The findings of this paper provide a practical foundation for future planning studies and demonstrate how cost-optimized retrofit strategies can support the scalable expansion of EV charging infrastructure in existing facilities.