The　paper　proposed　a　novel　Ant　Colony　Optimization　(ACO)　and　Quantum　Particle　Swarm　Optimization　(QPSO)　method　for　Spatial　Clustering　with　Obstacles　Constraints　(SCOC).　We　first　developed　AQPGSOD　using　ACO　and　QPSO　based　on　grid　model　to　obtain　obstructed　distance,　and　then　we　presented　a　new　QPKSCOC　based　on　QPSO　and　K-Medoids　to　cluster　spatial　data　with　obstacles.　The　experimental　results　show　that　AQPGSOD　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.　©　2009　IEEE.