Piezoresistive　acceleration　sensors　are　widely　used　in　various　fields　of　the　industrial　Internet　of　Things　because　of　their　lightweight,　fast　response,　and　small　size.　The　structural　sensitivity　of　the　sensor　affects　the　accuracy　of　the　measurement.　And　the　sensitivity　that　the　traditional　method　designs　are　only　a　feasible　solution,　not　an　optimal　solution.　Due　to　the　differences　in　factory　processes,　the　optimization　of　structural　sensitivity　is　an　NP-hard　problem.　To　solve　the　design　problem　of　structural　sensitivity,　we　adopt　the　swarm　intelligence　algorithm　in　this　paper,　and　we　design　a　model　for　the　structural　sensitivity　of　the　piezoresistive　acceleration　sensor.　In　addition,　an　improved　grasshopper　optimization　algorithm　(CC-GOA)　that　combines　chaos　strategy　and　Cauchy　mutation　is　proposed　to　optimize　the　structural　sensitivity　of　the　piezoresistive　acceleration　sensor,　and　the　structure　of　the　sensor　is　composed　of　four　beams　and　mass　block.　The　experiments　are　compared　with　six　well-known　algorithms　on　16　benchmark　functions　to　verify　the　algorithm　performance　of　CC-GOA,　and　then,　the　structural　sensitivity　of　the　piezoresistive　acceleration　sensor　is　optimized　by　CC-GOA.　The　results　indicate　that　the　piezoresistive　acceleration　sensor　is　designed　with　high　sensitivity　and　superiority.