In　many　practical　systems,　the　control　or　decision-making　is　usually　triggered　by　certain　events.　These　systems　are　classified　as　discrete　event　dynamic　systems　(DEDSs).　Considering　the　performance　optimization　of　these　systems,　a　new　optimization　framework　called　event-based　optimization　(EBO)　is　introduced　in　this　paper.　Compared　with　Markov　decision　process　(MDP),　one　of　the　main　characteristics　of　EBO　is　that　decisions　are　made　based　on　＂events＂　rather　than　states.　In　this　regard,　there　exist　a　number　of　advantages　for　EBO.　First,　an　event　usually　corresponds　to　a　set　of　state　transitions　with　some　common　properties.　Generally,　the　number　of　events　of　a　system　requiring　decisions　is　much　smaller　than　that　of　states.　Therefore,　the　EBO　approach　can　utilize　the　special　structure　of　systems　characterized　by　events　to　aggregate　the　potentials,　thus　alleviating　the　curses　of　dimensionality.　Second,　the　EBO　approach　applies　to　many　practical　problems　where　actions　are　required　only　when　certain　events　happen.　Such　problems　do　not　fit　well　the　standard　MDP　formulation　in　which　the　decisions　made　based　on　different　states　are　independent.　However,　for　that　cases,　the　same　action　may　be　taken　for　the　same　event,　which　may　correspond　to　many　different　states.　Based　on　the　basic　theory　of　MDP,　this　paper　is　addressed　around　three　aspects.　First,　we　briefly　review　the　basic　ideas　of　EBO　and　the　development　for　its　theory　and　applications.　Second,　we　introduce　the　simulation-based　policy　iteration　methods　for　EBO　based　on　the　performance　potentials　or　Q-factors;　Third,　a　case　study　is　conducted　on　the　coordination　of　electric　vehicle　charging　with　the　distributed　wind　power　generation　of　a　building,　which　aims　to　shed　some　lights　on　the　application　of　EBO　in　energy　Internet.　©　2018,　Editorial　Department　of　Control　Theory　&　Applications　South　China　University　of　Technology.　All　right　reserved.