Combined with back propagation (BP) neural network and genetic algorithm, a high-quality and low-cost process parameter optimization method was proposed. The flow rate of two blowtorch gases (H2-1, H2-2, H2-3, Ar-1, Ar-2, Ar-3, O2-1, O2-2, SiCl4) during the preparation of optical fiber preform core layer by vapor axial deposition (VAD) was selected as the input variable. The quality of the prepared optical fiber preform core layer was taken as the output variable in the established neural network model. The trained neural network model was combined with the genetic algorithm with global optimization ability, and the high quality core layer of optical fiber preform was taken as the optimization objective to obtain high quality gas parameters. The obtained parameters were selected at low cost, and the high quality and low cost process parameters were obtained. The experimental results showed that compared with the manual optimization results before optimization, the prepared optical fiber preform core layer met the high quality requirements and the cost was reduced by 22.19%.