The　stability　and　safety　of　building　Mechanical,　Electrical,　and　Plumbing　(MEP)　systems　are　significantly　influenced　by　the　steel　section　design　of　integrated　supports　and　hangers.　Traditional　design　methods,　reliant　on　empirical　steel　section　selection,　are　often　inefficient　and　can　result　in　unnecessary　material　usage.　This　paper　introduces　a　novel　multi-objective　optimization　approach　that　enhances　the　section　design　process　by　minimizing　material　costs　and　maximizing　bending　performance　of　supports　and　hangers.　Firstly,　the　Non-dominated　Sorting　Genetic　Algorithm　II　(NSGA-II)　is　employed　to　identify　an　array　of　optimal　cross-sectional　designs　that　reconcile　economic　considerations　with　structural　mechanical　performance.　Subsequently,　in　order　to　pinpoint　the　most　suitable　design　from　the　resulting　Pareto-optimal　scheme,　the　Method　of　Determining　the　Optimal　Scheme　(MDOS)　is　proposed.　MDOS　harnesses　expert　scoring,　the　Continuous　ordered　weighted　averaging　(COWA)　operator,　game　theory,　and　the　Technique　for　Order　Preference　by　Similarity　to　an　Ideal　Solution　(TOPSIS)　to　objectively　evaluate　and　prioritize　design　schemes　according　to　stakeholder　preferences　and　project-specific　criteria.　Finally,　the　optimization　results　are　analyzed　and　the　influence　of　different　spacing　of　supports　and　hangers　and　different　subjective　weight　factors　on　the　selection　of　cross-section　is　studied.　The　findings　reveal　that　the　method　can　precisely　determine　all　cross-section　designs　that　adhere　to　the　established　constraints,　achieving　a　4.09-fold　enhancement　in　design　efficiency.　It　was　observed　that　the　two　outlined　objectives　are　predominantly　affected　by　the　height　of　the　web.　The　distribution　trend　of　the　optimal　solution　value　of　the　independent　variable　is　almost　the　same　under　the　condition　of　different　values　of　the　supports　and　hangers　spacing.　With　the　change　of　subjective　weight,　the　cross　section　selection　focuses　on　three　optimal　schemes.　Therefore,　the　proposed　method　can　be　used　as　reference　for　similar　steel　design　optimization.