Metal-supported　solid　oxide　fuel　cell　(MS-SOFC)　is　an　emerging　technology　suitable　for　vehicular　applications.　Utilizing　the　off-gas　from　MS-SOFC　to　realize　methane　steam　reforming　is　an　attractive　approach　to　avoid　the　challenges　of　hydrogen　storage　and　transportation.　However,　the　operation　temperature　of　such　a　pre-reformer　will　be　lower　than　conventional　high-temperature　reformer.　How　to　optimize　the　geometric　parameters　of　such　a　pre-reformer　needs　to　be　clarified.　In　this　study,　a　data-driven　multi-objective　optimization　method　is　proposed.　The　critical　parameters　of　a　methane　steam　pre-reformer　are　determined　using　the　principal　component　analysis　based　on　Taguchi　method　and　a　data-driven　multi-objective　optimization　based　on　artificial　neural　network　(ANN)　and　NSGA-II　genetic　algorithm.　A　heterogeneous　mathematical　model　is　established　based　on　the　Langmuir-Hinshelwood　method.　The　effects　of　critical　geometric　parameters　of　the　pre-reformer　on　the　performance　under　intermediate-temperature　conditions　are　studied　using　Taguchi　orthogonal　experimental　design　and　analysis　of　variance　(ANOVA).　A　surrogate　ANN　model　for　predicting　the　performance　of　the　methane　pre-reformer　is　setup.　Then　a　multi-objective　genetic　algorithm　optimization　is　performed　based　on　the　surrogate　model　to　maximize　the　methane　conversion　rate　and　minimize　the　total　cost.　The　results　indicate　that　the　significances　of　the　four　parameters　are　different.　The　number　of　tubes　and　tube　length　demonstrate　the　most　significant　effects　on　the　methane　conversion　rate、hydrogen　production　yield　and　total　heat　transfer　cost,　whereas　the　bed　porosity　and　tube　inner　diameter　exhibit　the　lowest　degree　of　influences.　Using　a　surrogate　ANN　model　during　the　multi-objective　optimization　process　can　alleviate　the　computation　load　significantly　while　maintains　a　high　prediction　precision.　The　methane　conversion　rate　increases　by　9.5%　and　the　total　cost　declines　by　18.4%　compared　to　the　original　design.　©　2024　Elsevier　Ltd