For　electromagnetic　(EM)　optimization　of　microwave　components,　with　a　bad　computational　starting　point,　local　optimization　can　easily　fall　into　local　optima　that　cannot　satisfy　design　specifications.　In　this　case,　global　optimization　methods　can　be　utilized.　However,　global　optimization　methods　are　usually　time-consuming　because　of　its　relatively　low　convergence　rate.　In　this　paper,　an　efficient　EM　optimization　method　combining　Bayesian　optimization　(BO)　and　parallel　local　sampling　method　is　presented　to　solve　this　problem.　In　each　optimization　iteration,　the　presented　method　utilizes　both　the　predicted　optimal　solution　and　several　local　samples　in　an　adaptive　range　around　it　to　construct　the　surrogate　model　which　is　used　to　guide　the　optimization　process.　In　this　way,　the　presented　method　keeps　a　good　balance　of　exploration　and　exploitation　during　optimization.　Compared　to　existing　BO　which　only　utilizes　the　predicted　optimal　solution,　the　presented　BO　effectively　reduces　the　number　of　optimization　iterations,　thus　increasing　the　convergence　ability　of　optimization.　An　example　of　a　microwave　filter　optimization　is　presented　to　illustrate　the　presented　method.　©　2022　IEEE.