The design and optimization of a liquid cooling battery thermal management system (BTMS) are critical to maintaining optimal battery temperatures and ensuring longevity while balancing energy consumption. This study focuses on optimizing the heat transfer efficiency and energy consumption of a multi-channel cold plate in a liquid cooling BTMS for electric vehicles (EVs). Key variables, including steady flow velocity, pulsation amplitude, and pulsation frequency, were considered for optimization using the Multi-objective Jaya Algorithm (MOJAYA). A mathematical model was developed, and quadratic equations were derived for both the average heat transfer coefficient and energy consumption. The optimization process revealed trade-offs between these objectives, with the Pareto front providing a range of solutions that balance thermal performance and energy efficiency. Additionally, the results were validated using the augmented ε-constraint method (AUGMENCON) for optimal parameter selection. The findings demonstrate that MOJAYA can effectively optimize BTMS design, achieving improved cooling efficiency with minimal energy consumption.