Spatial　Clustering　with　Obstacles　Constraints　(SCOC)　has　been　a　new　topic　in　Spatial　Data　Mining　(SDM).　In　this　paper,　we　propose　a　Quantum　Particle　Swarm　Optimization　(QPSO)　method　for　SCOC.　In　the　process　of　doing　so,　we　first　developed　a　novel　spatial　obstructed　distance　using　QPSO　based　on　grid　model　to　obtain　obstructed　distance,　which　is　named　QPGSOD,　and　then　we　presented　a　new　QPKSCOC　based　on　QPSO　and　K-Medoids　to　cluster　spatial　data　with　obstacles　constraints.　The　experimental　results　show　that　QPGSOD　is　effective,　and　QPKSCOC　can　not　only　give　attention　to　higher　local　constringency　speed　and　stronger　global　optimum　search,　but　also　get　down　to　the　obstacles　constraints　and　practicalities　of　spatial　clustering;　and　it　performs　better　than　Improved　K-Medoids　SCOC　(IKSCOC)　in　terms　of　quantization　error　and　has　higher　constringency　speed　than　Genetic　K-Medoids　SCOC　(GKSCOC).