Traffic　signal　control　is　an　integral　component　of　an　intelligent　transportation　system　(ITS)　that　play　a　vital　role　in　alleviating　traffic　congestion.　Poor　traffic　management　and　inefficient　operations　at　signalized　intersections　cause　numerous　problems　as　excessive　vehicle　delays,　increased　fuel　consumption,　and　vehicular　emissions.　Operational　performance　at　signalized　intersections　could　be　significantly　enhanced　by　optimizing　phasing　and　signal　timing　plans　using　intelligent　traffic　control　methods.　Previous　studies　in　this　regard　have　mostly　focused　on　lane-based　homogenous　traffic　conditions.　However,　traffic　patterns　are　usually　non-linear　and　highly　stochastic,　particularly　during　rush　hours,　which　limits　the　adoption　of　such　methods.　Hence,　this　study　aims　to　develop　metaheuristic-based　methods　for　intelligent　traffic　control　at　isolated　signalized　intersections,　in　the　city　of　Dhahran,　Saudi　Arabia.　Genetic　algorithm　(GA)　and　differential　evolution　(DE)　were　employed　to　enhance　the　intersection＇s　level　of　service　(LOS)　by　optimizing　the　signal　timings　plan.　Average　vehicle　delay　through　the　intersection　was　selected　as　the　primary　performance　index　and　algorithms　objective　function.　The　study　results　indicated　that　both　GA　and　DE　produced　a　systematic　signal　timings　plan　and　significantly　reduced　travel　time　delay　ranging　from　15　to　35%　compared　to　existing　conditions.　Although　DE　converged　much　faster　to　the　objective　function,　GA　outperforms　DE　in　terms　of　solution　quality　i.e.,　minimum　vehicle　delay.　To　validate　the　performance　of　proposed　methods,　cycle　length-delay　curves　from　GA　and　DE　were　compared　with　optimization　outputs　from　TRANSYT　7F,　a　state-of-the-art　traffic　signal　simulation,　and　optimization　tool.　Validation　results　demonstrated　the　adequacy　and　robustness　of　proposed　methods.