<p>This study proposes an automated framework for steel warehouse design that simultaneously optimizes sectional and geometric properties. Unlike most existing approaches focusing only on weight minimization, the method also accounts for geometric configurations that influence wind loads and structural performance. To capture practical constraints, the framework integrates the Cutting Stock Problem (CSP), reflecting that steel members are supplied in standard lengths and cut on-site, often generating waste. In addition, girder lap splice locations are optimized to further minimize CSP waste. Two objectives are addressed: cost, defined as the total stock length including waste, and performance, measured by stress ratio. Optimization is performed using the Weighted Multi-Objective Symbiotic Organism Search (WMOSOS), which generates a Pareto front of non-dominated solutions. The framework couples an outer WMOSOS loop for structural optimization with an inner Particle Swarm Optimization loop for CSP. Two warehouse case study demonstrates its practicality and confirms WMOSOS superiority over other algorithms.</p>