The orientation of a part with respect to the build platform, placed in an additive manufacturing (AM) machine, is the build orientation (BO) and is an important attribute that influences the build time (in turn the cost), surface roughness (in turn the quality), and mechanical properties (in turn the functionality) of fused filament fabricated (FFF) parts. Many research articles have addressed these aspects, however, a notable research gap exists in exploring BO optimization in the context of multi-objective optimization. Some of these objectives are computationally expensive to evaluate as well. In the layer-by-layer addition of material, the rotation of part’s orientation about the two axes namely x and y in the build plane affects all the above mentioned objectives. This study formulates a BO optimization as a multi-objective problem, minimizing build time and surface roughness and solves the same using multi-objective genetic algorithm (MOGA). To address computational cost, low-fidelity models wherever applicable are used. The obtained solutions, are analyzed for pareto optimality and also appropriate visualization tools are used to help in trade-off and decision making. Lattice structures are known for their lightweight yet strong design, making them efficient in energy absorption while requiring minimal material. Built from networks of interconnected unit cells, they have become vital in various engineering applications. Due to their complex interconnected internal structures, they are often attempted to be built using AM. The study demonstrates the methodology of multi-objective optimization and its effectiveness using a case study in a lattice geometry namely body-centered cubic (BCC) lattice.