An enhanced multi-objective genetic algorithm is adopted to optimize secondary cooling process,which uses the probability method to select the operator and calculates crossover and mutation probability dynamically according to the fitness value. A better global optimal solution is achieved and the algorithm precision and overall performance improve greatly. In the secondary cooling optimization process,the model based on solidification heat transfer is solved by the variable spacing difference method. Compared to particle swarm algorithm and the traditional multi-objective genetic algorithm,the enhanced multi-objective genetic algorithm has the highest search efficiency and the minimum value function. In industrial applications,the optimized secondary cooling process can reduce the total consumption of water by 10% and improve the quality of casting billet, which meet the requirements of energy saving and consumption reduction.