Residual stresses and distortion in welded joints undermine the durability of the structure and prevent a correct assembly of the parts. The principal objective of this study is to find a solution to minimize the residual stresses and distortion induced by submerged arc welding process. Accordingly, first, a thermal simulation of the process was undertaken by the finite-element method, and the results were used to provide a mechanical solution. The mechanical solution determined residual stresses and distortion that were found to be consistent with experimental results. Next, drawing on the design of experiment method based on cooling time between first pass and second pass and the first and second pass welding speed, a set of training data was formed for the developed artificial neural network. The trained neural network was then used as input for the optimization algorithm. Single- and multi-objective Genetic Algorithm and single and multi-objective Harmony Search methods were used for optimization process. Results illustrate that artificial neural network and multi-objective optimization algorithms are excellent methods for optimizing the residual stresses and distortion caused by welding process. As it was proved in this study, the single-objective optimization of the welding process is effective in reducing both the residual stress and distortion. The double-objective optimization also contributed to reduce both residual stress and distortion with 4% (for residual stresses) and 26.56% (for distortion) in multi-objective Harmony Search which was the better algorithm based on the solution time. Given the contradiction of the residual stresses and distortion in the welding process, the double-objective algorithm was found to be less successful in minimizing the two target functions relative to the case with the two optimized separately.