In　this　article,　an　event-based　intelligent　critic　algorithm　is　developed　to　address　the　optimal　tracking　control　problem　for　a　type　of　discrete-time　nonlinear　systems.　The　nonlinear　optimal　tracking　control　design　is　replaced　by　solving　the　optimal　regulation　problem　of　the　error　system.　Then,　the　generalized　value　iteration　algorithm　is　employed　to　obtain　the　admissible　tracking　control　law　with　off-line　learning.　Next,　a　novel　triggering　condition　is　designed　to　reduce　the　update　times　of　the　controller　and　improve　the　resource　utilization.　It　is　emphasized　that　this　triggering　condition　is　established　based　on　the　iteration　of　the　time-triggered　mechanism.　Moreover,　in　order　to　realize　the　cost　guarantee　of　the　error　system,　the　real　cost　function　is　proved　to　possess　a　predetermined　upper　bound.　By　analysis,　it　is　shown　that　the　error　system　is　asymptotically　stable　while　the　tracking　error　and　the　sampling　signal　are　uniformly　ultimately　bounded　during　the　process　of　training　neural　networks.　Finally,　two　examples　are　conducted　to　demonstrate　the　effectiveness　of　the　proposed　algorithm.