Leveraging　human　insight　and　intuition　has　been　identified　as　having　the　potential　for　the　improvement　of　traditional　algorithmic　methods.　For　example,　in　a　video　game,　a　user　may　not　only　be　entertained　but　may　also　be　challenged　to　beat　the　score　of　another　player;　additionally,　the　user　can　learn　complicated　concepts,　such　as　multi-objective　optimization,　with　two　or　more　conflicting　objectives.　Traditional　methods,　including　Tabu　search　and　genetic　algorithms,　require　substantial　computational　time　and　resources　to　find　solutions　to　multi-objective　optimization　problems　(MOPs).　In　this　paper,　we　report　on　the　use　of　video　games　as　a　way　to　gather　novel　solutions　to　optimization　problems.　We　hypothesize　that　humans　may　find　solutions　that　complement　those　found　mechanically　either　because　the　computer　algorithm　did　not　find　a　solution　or　because　the　solution　provided　by　the　crowdsourcing　of　video　games　approach　is　better.　We　model　two　different　video　games　(one　for　the　facility　location　problem　and　one　for　scheduling　problems),　we　demonstrate　that　the　solution　space　obtained　by　a　computer　algorithm　can　be　extended　or　improved　by　crowdsourcing　novel　solutions　found　by　humans　playing　a　video　game.