Road　pricing　has　received　increasing　attention　for　traffic　congestion　mitigation,　but　traffic　demand　uncertainties　cause　a　profound　impact　on　its　effectiveness.　This　study　proposes　a　link-based　time-of-day　pricing　distributionally　robust　simulation-based　optimization　(DRSO)　model　to　minimize　the　worst-case　expected　total　travel　time　(TTT).　This　model　aims　to　answer　where　and　how　much　to　charge　under　origin-destination　(OD)　traffic　demand　uncertainties　that　are　described　by　an　ambiguity　set　of　probability　distributions,　and　is　solved　by　a　DRSO　method　with　two　stages.　The　first　stage　searches　an　infill　sample　after　building　two　stochastic　kriging　models　(i.　e.,　SKG-I　and　SKG-II)　to　map　the　relation　between　the　traffic　demand　uncertainty　parameter　and　the　average　TTT　for　each　decision　sample,　and　that　between　the　decision　variable　and　the　worstcase　average　TTT,　respectively.　The　second　stage　introduces　the　optimal　computational　budget　allocation　(OCBA)　method　to　distribute　more　simulation　replications　to　more　promising　combination　samples,　which　helps　to　improve　the　prediction　accuracy　of　SKGs　in　promising　regions.　The　DRSO　method　is　first　tested　with　an　M/M/1　queuing　problem,　and　numerical　results　show　its　outperformance　compared　with　one　counterpart　algorithm.　After　that,　it　is　applied　to　address　the　link-based　time-of-day　pricing　DRSO　problem　on　Anaheim　network　and　two　solution　algorithms　are　also　added　for　comparison.　Numerical　results　show　that　the　robust　toll　plan　solved　by　the　DRSO　method　outperforms　no　toll　plan　and　two　other　toll　plans　solved　by　the　comparison　algorithms　in　handling　traffic　dynamics　and　different　levels　of　traffic　demand　uncertainties.　In　conclusion,　the　DRSO　method　is　promising　to　address　the　costly　simulation-based　optimization　(SO)　problems　under　uncertainties　characterized　by　an　ambiguity　set　of　probability　distributions.