In　electromagnetic　(EM)　optimization　of　microwave　design,　a　computationally　bad　starting　point　usually　leads　the　local　optimization　process　to　be　stuck　into　local　optimum,　which　does　not　satisfy　the　design　specifications.　In　this　situation,　global　optimization　methods　can　be　an　alternative　to　achieve　the　final　optimal　solution.　However,　global　optimization　methods　always　suffer　from　a　relatively　low　convergence　rate.　To　address　this　problem,　we　propose　an　efficient　EM　optimization　technique　with　a　novel　parallel　local　sampling　strategy　and　Bayesian　optimization　(BO)　for　microwave　applications　in　this　article.　We　develop　a　new　parallel　local　sampling　strategy　to　increase　the　exploitation　ability　near　the　potential　optimal　solution　in　each　optimization　iteration　and　improve　the　convergence　of　the　entire　optimization　process.　The　local　sampling　range　in　each　iteration　is　determined　based　on　the　derivative　information　of　the　current　potential　optimal　solution.　While　conventional　BO　only　uses　the　information　of　potential　optimal　solutions　in　each　iteration　during　optimization,　we　propose　to　exploit　both　the　generated　local　samples　and　the　potential　optimal　solutions　together　to　build　a　surrogate　model　and　guide　the　optimization.　Therefore,　the　exploration　and　exploitation　during　the　proposed　EM　optimization　are　well　balanced,　and　the　entire　EM　optimization　process　is　effectively　accelerated　in　comparison　to　other　existing　global　methods.　Examples　of　EM　optimizations　of　microwave　components　are　used　to　demonstrate　the　proposed　technique.