The　infrastructure　resources　in　distributed　green　data　centers　(DGDCs)　are　shared　by　multiple　heterogeneous　applications　to　provide　flexible　services　to　global　users　in　a　high-performance　and　low-cost　way.　It　is　highly　challenging　to　minimize　the　total　cost　of　a　DGDC　provider　in　a　market　where　bandwidth　price　of　Internet　service　providers　(ISPs),　electricity　price　and　the　availability　of　renewable　green　energy　all　vary　with　geographic　locations.　Unlike　existing　studies,　a　Geographical　Scheduling　method　of　Multi-Application　Tasks　(GSMAT)　that　exploits　spatial　diversity　in　DGDCs　is　proposed　to　minimize　the　total　cost　of　their　provider　by　cost-effectively　scheduling　all　arriving　tasks　of　heterogeneous　applications　to　meet　tasks＇　delay　bound　constraints.　In　each　time　slot,　the　cost　minimization　problem　for　DGDCs　is　formulated　as　a　constrained　optimization　one　and　solved　by　the　proposed　Simulated-annealing-based　Bat　Algorithm　(SBA).　Trace-driven　experiments　demonstrate　that　GSMAT　achieves　lower　cost　and　higher　throughput　than　two　typical　scheduling　methods.