Abstarct: The prediction of rock sawability and determination of effective parameters are very important in the cost estimation and the best planning of the plants. Rock sawability depends on the machine characteristics and rock mechanical properties. In this study, first a new classification system was developed with the respect to rock mechanical properties such as uniaxial compressive strength, abrasivity index, mean Moh’s hardness, and Young’s modulus. The importance degree of mentioned parameters in the new classification system was determined by Fuzzy and Fuzzy Delphi Analytic Hierarchy Process methods. Using this classification system, the rock sawability index (RSi) of a variety groups of studied dimensional rocks (7 types of carbonate rock and 5 types of granite rock) was evaluated and classified into five categories. To verify the result of applied classification for ranking the rock sawability, studied rocks were sawn using a fully instrumented laboratory cutting rig at different feed rate and depth of cut at constant peripheral speed. During the sawing trials, the ampere and system vibration were monitored and calculated as performance characteristics of the saws. The results of sawing trials showed that the sawability ranking of studied rocks is correct. It was concluded that the sawability of dimensional rocks can reliably be ranked using the developed classification system. In the second step, the economic analysis was carried out on sawing process in the some factories located in Mahmod Abad Esfahan. The main factors that affect the dimensional rock sawing costs were calculated using the developed equations. Then, functions used in calculating sawing costs were developed using these equations. In additionally, a total cost model was generated for every dimensional rock product. Cost analyses of two different dimensional rock groups were made by using this model. Also sensitivity analyses of all cost parameters were created. A major conclusion to be drawn from this step was that the energy cost and wearing cost have the biggest effect on the carbonate and granite product cost model. The developed cost model from this study can be used for all carbonate and granite types, and can generate a cost analysis of wide dimensional rock production easily. In the third step of this study, the sawing parameters such as depth of cut and feed rat were optimized using genetic algorithm. A genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. In this study, the optimum machining parameters were determined for studied rocks using genetic algorithm. Finally, tow graphs were presented baxsed on rock sawability for selecting the optimum points of machining parameters (depth of cut and feed rate).