The beam-slab structure optimization problem (BSSOP) is a large-scale combinatorial problem that involves simultaneously determining component sizing, structural layout, and load-transfer topology. These interconnected aspects result in a vast design space with numerous variables and require careful consideration of complex boundary conditions to ensure reliable load-bearing performance. Many previous studies have addressed only isolated subproblems, often focusing solely on cost. Such narrow approaches fail to capture the multifaceted nature of real-world engineering demands and do not provide sufficient guidance for practical decision-making. In response, this study introduces an integrated workflow that utilizes a novel metaheuristic algorithm, referred to as MGRao, to optimize both cost and carbon emissions, thus aligning with the evolving sustainability criteria in structural engineering. To enhance user accessibility and the practical relevance of the results, Building Information Modeling (BIM) technology is incorporated to facilitate the automated identification and modeling of beam-slab structures, enabling intuitive visualization and assessment of optimization results. Through case studies, the proposed algorithm exhibits superior performance over conventional methods, and achieves balanced improvements in both cost efficiency and environmental impact. The findings highlight the algorithm’s robust adaptability and the workflow’s streamlined implementation, supporting its potential for direct application in real-world projects where multi-objective optimization and sustainability considerations are increasingly integral.