Additive manufacturing is a production technology based on creating three-dimensional parts directly from computer-aided design data layer-by-layer. In recent years, it has been used in many industries with the production of functional, high-quality metallic parts with the powder bed fusion process by laser. The build orientation of the three-dimensional part has a major impact on many factors such as part quality, waste amount, production time, and cost. In this study, a multi-objective optimization is carried out using non-dominated sorting genetic algorithm-II to simultaneously optimize different objectives that may conflict with each other, such as the amount of support structure and build time. Estimation methods are developed for computing the amount of support structure and the build time, which reflect the current state of the technology. With the developed method, build orientation is optimized for a complex part, and the wide range of alternative results are visualized and evaluated. The design for additive manufacturing knowledge required to correctly perform the build orientation process is eliminated by automating the pre-processing stage. Therefore, the contribution is made to the accessibility and sustainability of the PBF-L, which has high process costs by minimizing support structure volume and build time.