Battery thermal management has significant effect on the performance of electric vehicles (EVs) under high current rates. In this research, a comprehensive thermal analysis and multi-objective optimization design framework is proposed to enhance the thermal performance of a novel air–liquid cooling coupled battery pack under higher discharging rate (3C). Computational fluid dynamics (CFD) numerical calculation is utilized to compare the cooling efficiency of the battery pack designs. Furthermore, a surrogate model is generated by using Latin hypercube sampling (LHS) and support vector machine. The design parameters include different mini-channels’ mass flowrates and the air flow inlet velocity, the objectives are the temperature rise, temperature distribution, and the energy consumption. Sensitivity analysis results indicate that the air flow inlet velocity is the main factor affecting the temperature rise and temperature distribution, while the mass flowrates of mini-channels have important influence on the pressure drop. Finally, the nondominated sorting genetic algorithm-II (NSGA-II) is used to select the optimal battery pack design, the maximum temperature, and temperature standard deviation (TSD) get improved by 1.8 K and 0.06 K, respectively. And the energy consumption of the cooling system can be controlled within the appropriate range after optimization design.