More　and　more　large-scale　enterprises　choose　distributed　green　clouds　(DGCs)　to　cost-effectively　deploy　their　applications.　The　significant　increase　of　users＇　tasks　makes　it　highly　challenging　to　achieve　profit　maximization　for　a　DGC　provider　under　the　fact　that　prices　of　power　grid,　revenues,　and　the　amount　of　wind　and　solar　energy　in　DGCs　all　change　with　different　sites.　This　work　develops　a　Profit-Aware　Spatial　Task　Scheduling　(PASTS)　method　for　the　profit　maximization　of　a　DGC　provider.　PASTS　well　investigates　such　spatial　differences　of　these　mentioned　factors,　and　it　smartly　schedules　tasks　to　meet　their　response　time　constraints.　A　nonlinear　constrained　program　is　designed　and　tackled　by　a　hybrid　meta-heuristic　algorithm　that　combines　particle　swarm　optimization　with　genetic　mechanism　and　simulated　annealing.　Realistic　data-based　results　prove　that　PASTS　provides　higher　profit　and　throughput　than　two　recent　typical　algorithms.