Urban　subway,　because　of　its　speed　and　punctuality,　receives　considerable　popularity　and　injects　great　vitality　into　the　city＇s　economic　development.　However,　overcrowding　frequently　observed　in　subway　carriages　during　peak　hours　potentially　leads　to　an　increase　in　accident　risk,　while　there　are　still　some　spaces　available　in　buses　in　some　situations.　Such　imbalance　between　the　two　public　transits,　which　was　examined　based　on　the　load　ratios　reported　for　Beijing　public　transits　in　a　map　App,　motives　us　to　develop　a　periodic　stop-skipping　strategy　to　shift　the　excessive　demand　from　subway　to　bus　to　alleviate　the　overloaded　situations　while　to　reduce　the　unwanted　influence　on　the　passengers.　To　achieve　such　bi-objectives,　we　propose　a　non-linear　model　with　consideration　of　the　passengers＇　choice　under　the　stop-skipping　operation.　To　avoid　to　exhaustively　examine　all　stop　patterns,　the　stop　patterns　required　to　evaluate　are　identified　theoretically.　We　develop　a　genetic　algorithm　to　generate　the　optimal　plan,　with　the　triggering　mechanism　devised　to　give　the　priority　to　the　safety　objective.　With　the　calibrated　choice　model,　four　sets　of　numerical　experiments　were　performed　to　evaluate　the　proposed　approach.　The　results　indicate　that　our　approach　can　effectively　alleviate　the　overcrowding　while　the　efficiency　can　be　increased　in　some　circumstances　as　the　remaining　bus　capacity　is　utilized.　The　optimality　and　the　efficiency　of　the　algorithm　and　the　effectiveness　of　the　triggering　mechanism　were　also　verified.　The　proposed　approach　can　be　applied　as　an　effective　and　economical　way　to　rebalance　the　peak　demand　when　the　bus　capacity　is　surplus.