In　this　paper,　we　introduce　a　squared　metric　k-facility　location　problem　(SM-k-FLP)　which　is　a　generalization　of　the　squared　metric　facility　location　problem　(SMFLP)　and　k-facility　location　problem　(k-FLP).　In　the　SM-k-FLP,　we　are　given　a　client　set　C　and　a　facility　set　F　from　a　metric　space,　a　facility　opening　cost　f(i)　＞=　0　for　each　i　is　an　element　of　F,　and　an　integer　k.　The　goal　is　to　open　a　facility　subset　F　subset　of　F　with　vertical　bar　F　vertical　bar　＜=　k　and　to　connect　each　client　to　the　nearest　open　facility　such　that　the　total　cost　(including　facility　opening　cost　and　the　sum　of　squares　of　distances)　is　minimized.　Using　local　search　and　scaling　techniques,　we　offer　a　constant　approximation　algorithm　for　the　SM-k-FLP.