<p>Cost reduction is a major pillar of design for additive manufacturing, and a range of topology and build orientation optimization methodologies have been developed to address this goal, typically through support structure or overhang reduction. However, many factors contribute to the cost of an additively manufactured component, and approaches that consider only a single property during optimization fail to capture the full complexity of the design problem, leading to potentially suboptimal solutions. This paper proposes a comprehensive method for additive manufacturing design that uses four physical properties to model cost during topology and build orientation optimization: build height, surface area, overhang area, and support structure volume. These objectives are derived as differentiable functions of geometry and print orientation design variables to enable gradient-based optimization. The developed method is verified through both a benchmark example and an industry-level aircraft seat leg case study, with results demonstrating that the multi-objective approach generates designs that cannot be obtained with existing methods. Slicer software is used to measure the relationship between optimization objectives and real-world metrics (material use, printing time, and post-processing time). The analysis shows that the relative importance of optimization objectives varied significantly depending on the additive manufacturing method, highlighting the importance of customizing the objective function to effectively reduce cost, which can only be accomplished with the proposed comprehensive approach. By considering all cost objectives simultaneously, the proposed method reduced material use by 11–16% and print time by 12–54% compared to a sequential topology and build orientation optimization considering only overhang area.</p>