Achieving　high　precision　rapid　serial　visual　presentation　(RSVP)　task　often　requires　many　electrode　channels　to　obtain　more　information.　However,　the　more　channels　may　contain　more　redundant　informations　and　also　lead　to　its　limited　practical　applications.　Therefore,　it　is　necessary　to　reduce　the　number　of　channels　to　enhance　the　classification　performance　and　users　experience.　Furthermore,　cross-subject　generalization　has　always　been　one　of　major　challenges　in　EEG　channel　reduction,　especially　in　the　RSVP　paradigm.　Most　search-based　channel　selection　method　presented　in　the　literature　are　single-objective　methods,　the　classification　accuracy　is　usually　chosen　as　the　only　criterion.In　this　article,　the　idea　of　multi-objective　optimization　was　introduced　into　the　RSVP　channel　selection　to　minimize　two　objectives:　classification　error　and　the　number　of　channels.　By　combining　a　multi-objective　evolutionary　algorithm　for　solving　large-scale　sparse　problems　(SparseEA)　and　Hierarchical　Discriminant　Component　Analysis　(HDCA),　a　novel　channel　selection　method　for　RSVP　was　proposed.　After　that,　the　cross-subject　generalization　validation　through　the　proposed　channel　selection　method.The　proposed　method　achieved　an　average　classification　accuracy　of　95.41%　in　a　public　dataset,　which　is　3.49%　higher　than　HDCA.　The　classification　accuracy　was　increased　by　2.73%　and　2.52%　respectively.　Besides,　the　cross-subject　generalization　models　in　channel　selection,　namely　special-16　and　special-32,　on　untrained　subjects　show　that　the　classification　performance　is　better　than　the　Hoffmann　empirical　channels.The　proposed　channel　selection　method　could　reduce　the　calibration　time　in　the　experimental　preparation　phase　and　obtain　a　better　accuracy,　which　is　promising　application　in　the　RSVP　scenario　that　requires　low-density　electrodes.