The　enormous　energy　consumed　by　clouds　becomes　a　significant　challenge　for　cloud　providers　and　smart　grid　operators.　Due　to　performance　concerns,　applications　typically　run　in　different　clouds　located　in　multiple　sites.　In　different　clouds,　many　factors,　including　electricity　prices,　available　servers,　and　task　service　rates,　exhibit　spatial　variations.　Therefore,　it　is　important　to　manage　and　schedule　tasks　among　multiple　clouds　in　a　high-quality-of-service　and　low-energy-cost　manner.　This　work　proposes　a　task　scheduling　method　to　jointly　minimize　energy　cost　and　average　task　loss　possibility　(ATLP)　of　clouds.　A　problem　is　formulated　and　tackled　with　an　adaptive　biobjective　differential　evolution　based　on　simulated　annealing　to　determine　a　real-time　and　near-optimal　set　of　solutions.　A　final　knee　solution　is　further　chosen　to　specify　suitable　servers　in　clouds　and　task　allocation　among　web　portals.　Simulation　results　based　on　realistic　data　prove　that　less　average　loss　possibility　of　tasks,　and　smaller　energy　cost　is　obtained　with　it　than　its　widely　used　peers.　Note　to　Practitioners-This　work　considers　joint　optimization　of　both　ATLP　and　average　energy　cost　of　all　clouds.　It　is　of　great　significance　to　execute　tasks　among　multiple　clouds　by　jointly　allocating　all　tasks　among　multiple　web　portals　and　specifying　suitable　servers　in　different　clouds.　Yet,　it　is　challenging　to　achieve　joint　optimization　in　a　market　where　factors,　including　prices　of　electricity　and　available　servers,　show　spatial　variations.　Current　studies　are　coarse-grained　and　fail　to　jointly　achieve　average　energy　cost　minimization　and　quality-of-service　optimization　of　tasks.　In　this　work,　a　novel　algorithm　named　adaptive　simulated-annealing-based　biobjective　differential　evolution　is　proposed　for　an　energy　cost　and　quality-of-service-optimized　task　scheduling　strategy　in　a　real-time　manner.　Experiments　prove　that　it　realizes　lower　energy　cost　and　ATLP　compared　with　its　typical　widely　used　peers.　It　can　also　be　applied　to　other　industrial　areas,　including　smart　manufacturing,　Internet　of　Things,　and　smart　city.