The　integration　of　postal　and　passenger　transport　is　an　effective　measure　to　enhance　the　utilization　efficiency　of　passenger　and　freight　transportation　resources　and　to　promote　the　sustainable　development　of　urban–rural　transit　and　logistics.　This　paper　considers　the　uncertainty　in　passenger　and　freight　demand　as　well　as　transit　operation　times,　constructing　an　optimization　model　for　integrated　urban–rural　transit　and　postal　services　based　on　uncertainty　theory.　Passenger　and　freight　demand,　along　with　the　inverse　uncertain　distribution　of　events,　serve　as　constraints,　while　minimizing　passenger　travel　time　and　the　cost　for　passenger　transport　companies　are　the　optimization　objectives.　Taking　into　account　the　uncertainty　of　urban–rural　bus　travel　time,　the　scheduling　model　is　transformed　into　a　robust　form　for　scenarios　involving　single　and　multiple　origin　stations.　The　model　is　solved　using　an　improved　NSGA-II　(Nondominated　Sorting　Genetic　Algorithm　II)　to　achieve　effective　coordinated　scheduling　of　both　passenger　and　freight　services.　Through　a　case　study　in　Lotus　County,　Jiangxi　Province,　vehicle　routing　plans　with　varying　levels　of　conservativeness　were　obtained.　Comparing　the　results　from　different　scenarios,　it　was　found　that　when　the　total　vehicle　operating　mileage　increased　from　1.96%　to　62.26%,　passenger　transport　costs　rose　from　2.95%　to　62.66%,　while　the　total　passenger　travel　time　decreased　from　55.99%　to　172.31%.　In　terms　of　optimizing　costs　and　improving　passenger　travel　efficiency,　operations　involving　multiple　starting　stations　for　a　single　vehicle　demonstrated　greater　advantages.　Meanwhile,　at　a　moderate　level　of　robustness,　it　was　easier　to　achieve　a　balance　between　operational　costs　and　passenger　travel　time.　The　research　findings　provide　theoretical　support　for　improving　travel　conditions　and　resource　utilization　in　rural　areas,　which　not　only　helps　enhance　the　operational　efficiency　of　urban–rural　transit　but　also　contributes　positively　to　promoting　balanced　urban–rural　sustainable　development　and　narrowing　the　urban–rural　gap.　©　2024　by　the　authors.