The external static magnetic field has significant influence on the microstructure and mechanical properties of weld bead during 316L stainless steel laser welding. The quality of a weld joint of laser welding supported by external magnetic field (LWSMF) is significantly influenced by the processing parameters. Hence, how to choose appropriate processing parameters for a good-quality weld joint of LWSMF is significantly important. This paper develops a hybrid methodology by combining radial basis function neural network (RBFNN) and genetic algorithm (GA) to address this issue. Firstly, the bead appearance, which is the external manifestation of the weld geometrical imperfection and weld quality, is quantitatively described and selected as the indices to judge the weld quality of LWSMF. Then the physical experiments are conducted, where laser power (LP), laser welding speed (WS), and the flux density of magnetic field (MFD) are taken into consideration as the input process parameters. Secondly, the RBFNN model, as its excellent global prediction performance, is adopted to establish the relationships between the inputs and outputs. Thirdly, based on the constructed RBFNN model, the effects of different input parameters on the bead appearance are analyzed and the global process parameter space exploration is performed by using GA. At last, the verification experiments are conducted to verify the effectiveness of the calculated optimal process parameters. Results demonstrate that the spatter defects decrease and the grains are refined under the influence of external magnetic after optimization. On the whole, the proposed hybrid methodology shows great promise for improving the effectiveness and stability of LWSMF. Keywords: Laser welding, External magnetic field, Processing parameters optimization, 316L stainless stee