The　motion　planning　of　robots　faces　formidable　challenges　in　restricted　environments,　particularly　in　the　aspects　of　rapidly　searching　feasible　solutions　and　converging　toward　optimal　solutions.　This　article　proposes　workspace-guided　informed　tree　(WGIT*)　to　improve　planning　efficiency　and　ensure　high-quality　solutions　in　restricted　environments.　Specifically,　WGIT*　preprocesses　the　workspace　by　constructing　a　hierarchical　structure　to　obtain　critical　restricted　regions　and　connectivity　information　sequentially.　The　refined　workspace　information　guides　the　sampling　and　exploration　of　WGIT*,　increasing　the　sample　density　in　restricted　areas　and　prioritizing　the　search　tree　exploration　in　promising　directions,　respectively.　Furthermore,　WGIT*　utilizes　gradually　enriched　configuration　space　information　as　feedback　to　rectify　the　guidance　from　the　workspace　and　balance　the　information　of　the　two　spaces,　which　leads　to　efficient　convergence　toward　the　optimal　solution.　The　theoretical　analysis　highlights　the　valuable　properties　of　the　proposed　WGIT*.　Finally,　a　series　of　simulations　and　experiments　verify　the　ability　of　WGIT*　to　quickly　find　initial　solutions　and　converge　toward　optimal　solutions.