Intelligent　traffic　control　at　urban　intersections　is　vital　to　ensure　efficient　and　sustainable　traffic　operations.　Urban　road　intersections　are　hotspots　of　congestion　and　traffic　accidents.　Poor　traffic　management　at　these　locations　could　cause　numerous　issues,　such　as　longer　travel　time,　low　travel　speed,　long　vehicle　queues,　delays,　increased　fuel　consumption,　and　environmental　emissions,　and　so　forth.　Previous　studies　have　shown　that　the　mentioned　traffic　performance　measures　or　measures　of　effectiveness　(MOEs)　could　be　significantly　improved　by　adopting　intelligent　traffic　control　protocols.　The　majority　of　studies　in　this　regard　have　focused　on　mono　or　bi-objective　optimization　with　homogenous　and　lane-based　traffic　conditions.　However,　decision-makers　often　have　to　deal　with　multiple　conflicting　objectives　to　find　an　optimal　solution　under　heterogeneous　stochastic　traffic　conditions.　Therefore,　it　is　essential　to　determine　the　optimum　decision　plan　that　offers　the　least　conflict　among　several　objectives.　Hence,　the　current　study　aimed　to　develop　a　multi-objective　intelligent　traffic　control　protocol　based　on　the　non-dominated　sorting　genetic　algorithm　II　(NSGA-II)　at　isolated　signalized　intersections　in　the　city　of　Dhahran,　Kingdom　of　Saudi　Arabia.　The　MOEs　(optimization　objectives)　that　were　considered　included　average　vehicle　delay,　the　total　number　of　vehicle　stops,　average　fuel　consumption,　and　vehicular　emissions.　NSGA-II　simulations　were　run　with　different　initial　populations.　The　study　results　showed　that　the　proposed　method　was　effective　in　optimizing　considered　performance　measures　along　the　optimal　Pareto　front.　MOEs　were　improved　in　the　range　of　16%　to　23%　compared　to　existing　conditions.　To　assess　the　efficacy　of　the　proposed　approach,　an　optimization　analysis　was　performed　using　a　Synchro　traffic　light　simulation　and　optimization　tool.　Although　the　Synchro　optimization　resulted　in　a　relatively　lower　signal　timing　plan　than　NSGA-II,　the　proposed　algorithm　outperformed　the　Synchro　optimization　results　in　terms　of　percentage　reduction　in　MOE　values.　©　2020　by　the　authors.