In　this　paper,　we　proposed　a　new　efficient　approach　to　construct　confidence　intervals　for　the　location　and　scale　parameters　of　the　generalized　Pareto　distribution　(GPD)　when　the　shape　parameter　is　known.　The　superiority　of　our　method　is　that　the　distributions　of　pivots　are　exact,　but　not　approximate　distributions.　The　proposed　interval　estimation　provides　the　shortest　interval　for　the　GPD　parameter　whether　or　not　the　confident　distribution　of　the　pivot　is　symmetric.　We　first　estimate　the　location　and　scale　parameters　of　the　GPD　using　least　squares　and　then,　construct　confidence　intervals　based　on　the　equal　probability　density　principle.　The　results　of　various　simulation　studies　illustrate　that　our　interval　estimators　show　the　better　performance　than　competing　method.