Computer-aided　process　planning　is　an　important　interface　between　computer-aided　design　and　computer-aided　manufacturing　in　computer-integrated　manufacturing　environments.　In　this　paper,　the　complicated　process　planning　is　modeled　as　a　combinatorial　optimization　problem　with　constraints,　and　a　hybrid　graph　and　genetic　algorithm　(GA)　approach　has　been　developed.　The　approach　deals　with　process　planning　problems　in　a　concurrent　manner　by　simultaneously　considering　activities　such　as　sequencing　operations,　selecting　manufacturing　resources,　and　determining　setup　plans　to　achieve　the　global　optimal　objective.　Graph　theory　accompanied　with　matrix　theory,　as　the　basic　mathematical　tool　for　operation　sequencing,　is　embedded　into　the　main　frame　of　GA.　The　precedence　constraints　between　operations　are　formulated　in　an　operation　precedence　graph　(OPG).　The　initial　population　composed　of　all　feasible　solutions　is　generated　by　an　elaborately　designed　topologic　sort　algorithm　to　the　OPG.　A　modified　crossover　operator　guaranteeing　only　feasible　offspring　generated　is　used,　two　types　of　mutation　strategies　are　adopted,　and　a　heuristic　algorithm　is　applied　to　adjust　the　infeasible　plan　generated　by　the　mutation　operator　to　the　feasible　domain.　A　case　study　has　been　carried　out　to　demonstrate　the　feasibility　and　efficiency　of　the　proposed　approach.