Wire Arc Additive Manufacturing (WAAM) holds significant application value in the aerospace field, but the instability of heat input leads to prominent issues such as poor geometric conformity and numerous internal defects in aluminum alloy thin-walled components. To address the limitations of traditional methods in multi-physics coupling optimization, this study proposes a data-driven solution: by constructing a dataset of process parameters (current, scanning speed, wire feed rate) and forming quality (path/interlayer wall thickness consistency, porosity), a BP neural network model is established and optimized using the GA genetic algorithm, combined with the NSGA-II algorithm for multi-objective optimization. The results show that the optimized GABP model significantly improves the prediction accuracy of path wall thickness consistency and porosity, but the optimization effect on interlayer wall thickness consistency prediction is limited. Four types of optimization strategies are proposed based on the 50 sets of Pareto solutions obtained through NSGA-II, and validation tests indicate a model prediction error of 8.89%, accurately achieving the collaborative optimization of forming quality indicators. This method provides intelligent decision-making support for the forming quality control of WAAM thin-walled components.