The production of sapphire single crystals with high quality and low cost is an important goal of the sapphire industry. The quality of seeding has a direct impact on the final growth quality of sapphire crystals when sapphire is prepared using the Kyropoulos method. Keeping the crystal/melt contact stable is essential for lowering the number of seeding defects. In order to reduce the preparation cost of sapphire, an optimization strategy integrating computational fluid dynamics, artificial neural networks, and genetic algorithm is proposed to optimize the growth parameters of crystal seeding. The optimization goals are to stabilize the crystal/melt growth interface and increase seeding effectiveness. Three growth parameters, namely, melt temperature, seed crystal rotation speed and pulling speed, are selected as optimization variables. The samples produced by numerical calculation are combined with the actual production data to train and test the neural networks. The genetic algorithm is coupled with artificial neural networks to solve the multi-objective conflict between improving seeding efficiency and reducing the formation of crystal defects in the seeding process. The influence of the mechanism of different combinations of seeding parameters on seeding quality is discussed, and the optimization of seeding growth process is realized. The results show that the optimized combination of parameters gained in this study can improve the seeding speed and ensure the stability of the solid-liquid interface. This work is helpful to reduce the formation of seeding defects, improve the seeding quality, and shorten the seeding time.