This　paper　proposes　a　push　and　pull　search　(PPS)　framework　for　solving　constrained　multi-objective　optimization　problems　(CMOPs).　To　be　more　specific,　the　proposed　PPS　divides　the　search　process　into　two　different　stages:　push　and　pull　search　stages.　In　the　push　stage,　a　multi-objective　evolutionary　algorithm　(MOEA)　is　used　to　explore　the　search　space　without　considering　any　constraints,　which　can　help　to　get　across　infeasible　regions　very　quickly　and　to　approach　the　unconstrained　Pareto　front.　Furthermore,　the　landscape　of　CMOPs　with　constraints　can　be　probed　and　estimated　in　the　push　stage,　which　can　be　utilized　to　conduct　the　parameter　setting　for　the　constraint-handling　approaches　to　be　applied　in　the　pull　stage.　Then,　a　modified　form　of　a　constrained　multi-objective　evolutionary　algorithm　(CMOEA),　with　improved　epsilon　constraint-handling,　is　applied　to　pull　the　infeasible　individuals　achieved　in　the　push　stage　to　the　feasible　and　non-dominated　regions.　To　evaluate　the　performance　regarding　convergence　and　diversity,　a　set　of　benchmark　CMOPs　and　a　real-world　optimization　problem　are　used　to　test　the　proposed　PPS　(PPS-MOEA/D)　and　state-of-the-art　CMOEAs,　including　MOEA/D-IEpsilon,　MOEA/D-Epsilon,　MOEA/D-CDP,　MOEA/D-SR,　C-MOEA/D　and　NSGA-II-CDP.　The　comprehensive　experimental　results　show　that　the　proposed　PPS-MOEA/D　achieves　significantly　better　performance　than　the　other　six　CMOEAs　on　most　of　the　tested　problems,　which　indicates　the　superiority　of　the　proposed　PPS　method　for　solving　CMOPs.　©　2018　Elsevier　B.V.