This　article　proposes　a　novel　parallel　gradient-based　electromagnetic　(EM)　optimization　approach　to　microwave　components　using　adjoint-sensitivity-based　neuro-transfer　function　(neuro-TF)　surrogate.　In　the　proposed　technique,　the　surrogate　model　is　trained　using　not　only　the　input-output　behavior　but　also　the　adjoint　sensitivity　information　generated　from　the　EM　simulation　simultaneously.　By　exploiting　adjoint　EM　sensitivity　for　surrogate　modeling,　the　proposed　technique　can　obtain　accurate　surrogate　models　with　larger　valid　range　using　the　same　amount　of　fine　model　evaluations　compared　with　the　existing　gradient-based　surrogate　optimization　without　adjoint　sensitivity.　Furthermore,　because　the　surrogate　model　is　developed　using　adjoint　EM　sensitivity,　the　gradients　calculated　using　the　developed　surrogate　model　in　the　proposed　technique　are　much　more　accurate.　The　accurate　gradients　lead　to　further　speedup　of　the　surrogate　optimization　and　improved　quality　of　surrogate　optimal　solution　in　each　surrogate　optimization　iteration.　Since　the　surrogate　model　is　valid　in　a　large　neighborhood　and　the　gradients　are　sufficiently　accurate,　the　proposed　technique　can　achieve　the　optimal　EM　solution　faster　than　the　existing　gradient-based　surrogate　optimization　without　adjoint　sensitivity.　Three　examples　of　EM　optimizations　of　microwave　components　are　used　to　demonstrate　the　proposed　technique.