Thermal energy storage enhances grid stability and promotes the use of renewable energy resources. As energy storage systems become increasingly integrated into power grids, efficient micro-grid management is essential for effectively managing these resources. Optimizing the charging and discharging processes of thermal energy storage is crucial to improving the techno-economic performance of energy systems and supporting effective demand response strategies. This study addresses the challenge of optimizing multi-vector energy systems by integrating solar photovoltaic generation, thermo-chemical thermal energy storage, and resistive heating. A multi-objective optimization framework, employing the Non-dominated Sorting Genetic Algorithm, was developed to simultaneously minimize total annual cost and carbon emissions for a university building case study. The results demonstrate that strategically sizing and operating the integrated system, using dynamic charging and discharging strategies based on electricity price and load profiles, can achieve a ∼ 7.4% reduction in annual operating costs and a ∼ 5.1% reduction in carbon emissions compared to the baseline. This work highlights the potential of metaheuristic optimization for the design and operation of sustainable and cost-effective energy building systems.