This　paper　develops　a　model-free　approach　to　solve　the　event-triggered　optimal　consensus　of　multiple　Euler-Lagrange　systems　(MELSs)　via　reinforcement　learning　(RL).　Firstly,　an　augmented　system　is　constructed　by　defining　a　pre-compensator　to　circumvent　the　dependence　on　system　dynamics.　Secondly,　the　Hamilton-Jacobi-Bellman　(HJB)　equations　are　applied　to　the　deduction　of　the　model-free　event-triggered　optimal　controller.　Thirdly,　we　present　a　policy　iteration　(PI)　algorithm　derived　from　RL,　which　converges　to　the　optimal　policy.　Then,　the　value　function　of　each　agent　is　represented　through　a　neural　network　to　realize　the　PI　algorithm.　Moreover,　the　gradient　descent　method　is　used　to　update　the　neural　network　only　at　a　series　of　discrete　event-triggered　instants.　The　specific　form　of　the　event-triggered　condition　is　then　proposed,　and　it　is　guaranteed　that　the　closed-loop　augmented　system　under　the　event-triggered　mechanism　is　uniformly　ultimately　bounded　(UUB).　Meanwhile,　the　Zeno　behavior　is　also　eliminated.　Finally,　the　validity　of　this　approach　is　verified　by　a　simulation　example.