In　this　paper,　we　consider　the　problem　of　variable　selection　and　model　detection　in　varying　coefficient　models　with　longitudinal　data.　We　propose　a　combined　penalization　procedure　to　select　the　significant　variables,　detect　the　true　structure　of　the　model　and　estimate　the　unknown　regression　coefficients　simultaneously.　With　appropriate　selection　of　the　tuning　parameters,　we　show　that　the　proposed　procedure　is　consistent　in　both　variable　selection　and　the　separation　of　varying　and　constant　coefficients,　and　the　penalized　estimators　have　the　oracle　property.　Finite　sample　performances　of　the　proposed　method　are　illustrated　by　some　simulation　studies　and　the　real　data　analysis.