Distributed　clouds　(DCs)　often　require　a　huge　amount　of　energy　to　provide　multiple　services　to　users　around　the　world.　Users　bring　revenue　to　DC　providers　based　on　the　quality　of　service　(QoS)　of　tasks.　These　tasks　are　transmitted　to　DCs　through　many　available　Internet　service　providers　(ISPs)　with　different　bandwidth　prices　and　capacities.　Besides,　power　grid　prices,　and　green　energy　in　different　DCs　differ　with　different　geographical　sites.　Consequently,　it　is　challenging　to　execute　tasks　among　DCs　in　a　high-QoS　and　high-profit　way.　This　work　proposes　a　bi-objective　optimization　algorithm　to　maximize　the　profit　of　a　DC　provider,　and　minimize　the　loss　possibility　of　all　tasks　by　specifying　the　allocation　of　tasks　among　different　ISPs,　and　task　service　rates　of　each　DC.　A　constrained　optimization　problem　is　given　and　solved　by　a　novel　Simulated-annealing-based　Bi-objective　Differential　Evolution　(SBDE)　algorithm　to　produce　a　close-to-optimal　Pareto　set　of　solutions.　The　minimum　Manhattan　distance　is　further　used　to　obtain　a　knee　solution,　and　it　determines　Pareto　optimal　service　rates　and　task　allocation　among　ISPs.　Realistic　trace-driven　results　demonstrate　that　SBDE　realizes　less　loss　possibility　of　tasks,　and　higher　profit　than　several　state-of-the-art　scheduling　algorithms.