The　significant　growth　in　the　number　and　types　of　tasks　of　heterogeneous　applications　in　green　cloud　data　centers　(GCDCs)　dramatically　increases　their　providers＇　revenue　from　users　as　well　as　energy　consumption.　It　is　a　big　challenge　to　maximize　such　revenue,　while　minimizing　energy　cost　in　a　market　where　prices　of　electricity,　availability　of　renewable　power　generation,　and　behind-The-meter　renewable　generation　contract　models　differ　among　the　geographical　sites　of　the　GCDCs.　A　multiobjective　optimization　method　that　investigates　such　spatial　differences　in　the　GCDCs　is　for　the　first　time　proposed　to　trade　off　such　two　objectives　by　cost-effectively　executing　all　tasks　while　meeting　their　delay　constraints.　In　each　time　slot,　a　constrained　biobjective　optimization　problem　is　formulated　and　solved　by　an　improved　multiobjective　evolutionary　algorithm　based　on　decomposition.　Realistic　data-based　simulations　prove　that　the　proposed　method　achieves　a　larger　total　profit　in　faster　convergence　speed　than　the　two　state-of-The-Art　algorithms.　Note　to　Practitioners-This　article　considers　the　tradeoff　between　profit　maximization　and　energy　cost　minimization　for　the　green　cloud　data　center　(GCDC)　providers　while　meeting　the　delay　constraints　of　all　tasks.　Current　task-scheduling　methods　fail　to　take　the　advantage　of　spatial　variations　in　many　factors,　e.g.,　prices　of　electricity　and　availability　of　renewable　power　generation　at　geographically　distributed　GCDC　locations.　As　a　result,　they　fail　to　execute　all　tasks　of　heterogeneous　applications　within　their　delay　bounds　in　a　high-revenue　and　low-energy-cost　manner.　In　this　article,　a　multiobjective　optimization　method　that　addresses　the　disadvantages　of　the　existing　methods　is　proposed.　It　is　realized　by　a　proposed　intelligent　optimization　algorithm.　Simulations　demonstrate　that　in　comparison　with　the　two　state-of-The-Art　scheduling　algorithms,　the　proposed　one　increases　the　profit　and　reduces　the　convergence　time.　It　can　be　readily　implemented　and　integrated　into　actual　industrial　GCDCs.　©　2004-2012　IEEE.