The setting of micro resistance spot welding parameters plays an important role in the shear force and peel force, through the range analysis of orthogonal test and the influence of process parameters on the shear force and peel force of thickness of 0.05 mm foil TC1 resistance spot welding was investigetal. By giving the corresponding values of shear force and peeling force, the bi-objective optimization is transformed into a single hybrid objective optimization, BP neural network and genetic algorithm are combined to optimize the process parameters. A prediction model of the mechanical properties of solder joints based on BP neural network is established.The prediction results show that the error is less than 4%, indicating that the network model has higher prediction accuracy and ability.It can predict the mechanical properties of solder joints accurately. At the same time, with the global optimization ability of genetic algorithm, the parameters of spot welding are optimized, and the optimum combination of welding parameters is obtained:welding current 800 A, electrode pressure 8.89 N, ramping time 1.608 ms, welding time 8 ms, hybrid target force value 55.73 N. By comparing the results of orthogonal test, genetic algorithm optimization can get better comprehensive mechanical performance.