Gradient-based　surrogate　optimization　usually　has　a　fast　convergence　capability.　However,　it　can　be　easily　stuck　in　local　minima,　especially　when　the　electromagnetic　(EM)　response　of　the　starting　point　is　far　away　from　the　design　specification.　This　article　proposes　a　novel　feature　and　EM　sensitivity　coassisted　neuro-transfer　function　(TF)　surrogate　optimization　for　microwave　filter　design.　The　proposed　technique　introduces　EM　sensitivity　information　into　the　pole-zero-based　neuro-TF　with　feature　parameters　for　the　first　time.　New　formulations　are　derived　for　establishing　the　adjoint　neuro-TF　model　with　poles　and　zeros　as　the　transfer　function　parameters.　More　accurate　gradients　of　the　neuro-TF　outputs　with　respect　to　design　variables　are　subsequently　achieved　by　the　training　with　EM　sensitivity.　Two　sets　of　feature　parameters,　i.e.,　feature　frequencies　and　feature　heights,　are　used　in　the　proposed　technique.　The　adjoint　feature　frequencies　are　proposed　as　the　gradients　of　feature　frequencies,　which　are　calculated　using　the　trained　adjoint　neural　network　outputs.　New　formulations　are　further　derived　for　the　gradients　of　feature　heights　using　both　trained　adjoint　neuro-TF　and　adjoint　neural　network　outputs.　To　improve　the　robustness　of　the　optimization　process,　the　trust　region　algorithm　is　also　introduced.　By　the　coassistance　of　feature　parameters　and　EM　sensitivities,　the　proposed　technique　can　achieve　a　further　acceleration　over　the　existing　feature-assisted　techniques.　This　article　utilizes　three　microwave　filter　examples　to　demonstrate　this　technique.