In　this　research,　the　collation　delay　(CD)　and　makespan　minimization　problem　in　a　mail-order　pharmacy　automated　distribution　(MOPAD)　system　are　studied.　The　MOPAD　systems,　which　are　integrated　with　pharmaceutical　auto-dispenser　machines,　auto-packer　machines,　and　conveyor,　are　utilized　to　fulfill　the　increasing　prescription　demand　in　recent　years.　The　motivation　of　this　research　is　derived　from　the　practical　deadlock　problem　in　a　MOPAD　system　of　the　central　fill　pharmacies　(CFP).　Most　of　the　customer　orders　consist　of　multiple　medications,　which　need　to　be　collated　together　before　being　packaged　and　shipped.　The　CD　is　defined　as　the　fulfillment　completion　time　difference　between　the　first　and　last　medications　within　the　same　order,　which　is　a　critical　factor　of　the　MOPAD　systems　throughput.　When　CD　is　minimized,　the　makespan　often　increases.　Therefore,　alternative　scheduling　solutions　are　often　needed　to　balance　the　CD　and　makespan　in　the　MOPAD　system.　This　paper　presents　the　trade-off　solutions　between　minimizing　CD　and　the　makespan.　Three　multi-objective　genetic　algorithms　with　a　three-tuples　chromosome　design,　including　Vector　Evaluated　Genetic　Algorithm　(VEGA),　Multi-Objective　Genetic　Algorithm　(MOGA),　and　non-dominated　sorted　genetic　algorithm-II　(NSGA-II),　are　implemented　and　compared　under　various　system　settings.　Compared　to　the　current　implemented　longest　processing　time　(LPT)　heuristic,　three　multi-objective　genetic　algorithms　save　the　CD　by　95.67　%,　but　only　increase　the　makespan　by　5.62　%　on　average.　The　results　also　show　that　the　NSGA-II　provided　the　best　frontier.