A　growing　number　of　organizations　are　hosting　their　software　applications　in　distributed　data　centers　(DCs)　in　the　cloud,　for　faster　response　time　and　higher　energy　efficiency.　The　dramatic　increase　of　user　tasks,　however,　poses　a　significant　challenge　on　DC　providers　to　retain　users＇　expectations　on　both　aspects.　To　tackle　this　challenge,　this　work　first　formulates　the　problem　into　a　constrained　biobjective　optimization　problem.　A　biobjective　algorithm,　named　simulated-annealing-based　adaptive　differential　evolution　(SADE),　is　presented　to　simultaneously　reduce　both　the　response　time　of　tasks　and　energy　cost.　Meanwhile,　a　method　of　minimal　Manhattan　distance　is　adopted　to　search　for　a　final　knee,　for　achieving　a　good　balance　between　response　time　minimization　and　energy　cost　reduction.　Experimental　results　on　real-life　datasets,　i.e.,　the　electricity　prices　and　tasks　collected　from　a　Google　cluster　trace,　have　proved　that　SADE　yields　less　task　response　time　and　lower　energy　cost　compared　with　state-of-the-art　algorithms.