Organic　Rankine　cycle　(ORC)　is　a　promising　technology　to　convert　low-　and　medium-temperature　energy　into　power.　Identifying　the　optimal　working　fluids　and　heat　source　temperature　are　always　the　focuses　in　the　ORC　field.　This　paper　presents　a　new　methodology　to　evaluate　the　thermodynamic　performance　of　ORC　with　different　working　fluids　and　identify　the　optimal　heat　source　temperature.　Initially,　the　parameterization　model　is　developed　to　characterize　the　working　fluids　by　thermodynamic　property　parameters　including　critical　temperature　(T-c),　critical　pressure　(p(c)),　acentric　factor　(omega),　and　ideal　gas　isobaric　heat　capacity　(c(p)(0)).　Subsequently,　the　simultaneous　optimization　of　thermodynamic　property　parameters　and　cycle　parameters　is　conducted　to　obtain　the　thermodynamic　performance　limits　of　simple　and　regenerative　ORCs　at　six　typical　geothermal　heat　source　temperatures.　By　comparing　the　thermodynamic　performance　limits　of　ORC　under　different　heat　source　temperatures,　the　optimal　heat　source　temperature　is　identified.　Then,　ten　commonly　used　working　fluids　are　selected　as　reference　working　fluids,　and　the　thermodynamic　property　parameters　comparisons　between　reference　and　ideal　working　fluids,　which　can　be　characterized　by　the　optimized　thermodynamic　property　parameters,　are　investigated.　Finally,　multiple　linear　regression　models　are　developed　to　evaluate　the　thermodynamic　performance.　The　numerical　differences　of　thermodynamic　property　parameters　between　the　ideal　reference　and　reference　working　fluids　are　chosen　as　initial　variables,　while　the　thermal　efficiency　and　volumetric　power　output　are　used　as　thermodynamic　performance　indicators.　The　results　show　that　the　optimal　heat　source　temperature　is　250　degrees　C,　which　is　independent　of　cycle　configuration.　The　thermodymtm　ic　performance　of　ORCs　can　be　evaluated　accurately　by　the　multiple　linear　regression　models.　The　maximum　relative　error　of　the　multiple　linear　regression　models　is　3.02%.　Moreover,　T-c　is　the　most　dominant　thermodynamic　property　parameter.