Cost-effective　task　scheduling　is　an　important　operation　in　green　infrastructure-as-a-service　clouds　(GICs)　as　the　energy　consumed　by　users＇　tasks　is　drastic.　The　irregular　task　arrival　forces　private　GIC　to　adopt　hybrid　clouds　to　outsource　some　tasks　to　dynamic　and　reliable　virtual　machines　(VMs)　of　public　external　clouds.　However,　temporal　differences　in　revenue,　electricity　prices,　wind　and　solar　energy,　and　VM　running　prices　of　public　external　clouds　make　it　difficult　to　dispatch　all　tasks　in　a　cost-effective　way　while　satisfying　users＇　specified　response　time　constraints.　Unlike　existing　methods,　we　propose　a　multiqueue　scheduling　(MQS)　method　that　investigates　such　temporal　differences　in　hybrid　GICs　(HGICs).　Specially,　this　work　first　gives　mathematical　relations　between　rejected　tasks　and　service　rates　of　servers　in　private　GIC.　In　each　iteration　of　MQS,　this　paper　formulates　a　profit　maximization　problem　for　HGIC　and　solves　it　by　a　novel　meta-heuristic　optimization　method　by　combing　simulated　annealing,　particle　swarm　optimization,　and　genetic　algorithm.　Trace-driven　experiments　based　on　real-life　data　demonstrate　that　profit　and　throughput　of　MQS　are　larger　than　typical　task　scheduling　algorithms　while　meeting　tasks＇　response　time　constraints.