Hybrid welding is a process used in aeronautical materials to obtain benefits such as complete penetration, narrow heat affected zones, reduce filler material used, among others, mainly due the ability of the process to control filler-metal additions and heat input in materials such as steels, titanium and aluminum alloys. Recently a new industrial revolution is taken place called manufacturing 4.0, the aeronautical and automotive industry have a great interest in all the pillars which making it up, one of the principal pillars is the big data analysis such as welding parameters applied in advances welding processes. The present work describes a welding optimization applying non-dominated sorting genetic algorithm-III (NSGA-III) to find and predict depth penetration (DP) and mechanical properties, specifically ultimate tensile strength (UTS) in ASTM 1520 steel welded by a hybrid laser arc welding with the objective to improve the weld quality. A diverse experiment were used in order to obtain a suitable model, considering welding speed (WS), wire feed rate (WF), voltage (V), current (A) and laser power (P). The experimental results demonstrated that the pareto front values obtained by NSGA-III improve the DP and UTS. Microstructural phases and mechanical properties were discuss to complement the values obtained and chosen.