Abstract: Core loading pattern (LP) optimization can enhance the safety and economy, of reactors. However, its optimization process demands a considerable amount of time-consuming computations and extensive manual experience. Aiming at the rapid evaluation issue of core LP schemes, this study employed the fully connected neural network (FCNN) and convolutional neural network (CNN) to establish a rapid prediction model for the neutronic parameters of the Daya Bay first-cycle core, enabling a rapid assessment of the pressurized water reactor core LP scheme. The generalization ability and accuracy of the prediction model were verified through the core calculation program DONJON and the core program. Regarding the global search issue of the core optimization scheme, the non-dominated sorting genetic algorithm (NSGA) was utilized to carry out multi-objective optimization of the LP scheme for the Daya Bay first-cycle core, and the optimization effect was enhanced by adjusting the parameters of the NSGA algorithm. The results suggest that the NSGA series of algorithms can be applied to various types of nuclear design optimization problems, including core LP optimization, and can make up for the poor global nature of manual search schemes. Simultaneously, the parallel optimization of the NSGA algorithm in combination with supercomputing can significantly enhance the optimization efficiency. For the problem of rapid optimization of the core LP scheme, a joint optimization program was developed by leveraging the neural network prediction model based on GPU parallelism and the NSGA algorithm, achieving rapid optimization of the Daya Bay first-cycle core LP scheme. By comparing the optimization results of the joint optimization program with those of "DONJON + NSGA", it is shown that the joint optimization program of the neural network-genetic algorithm can obtain core LP schemes with relatively similar results while reducing the optimization time by over 99%.