This paper has been applied for designing of developed optimal Self-compacted Fiber Reinforced Concrete (SCFRC) incorporating polypropylene for construction of building industry. Concrete is a hybrid material incorporating of cementitious materials, water, aggregates and additive materials. These comprise the constituent materials of concrete. With the development of using materials in construction engineering, concrete blends have been the most widely utilized for building industry in all over the world. The elimination of vibration to compact concrete during placing through the use of self-compacting concrete leads to substantial advantages related to better homogeneity and hence, quality of the construction, improving of the workability, environmentally purposes and enhancement of the productivity by increasing the rate of construction. Over the last few years, the manifested developments of superplasticizers technology allowed great achievements in the conception of concrete Mixtures exhibiting selfcompacted performance. Since the last decades, some techniques have been presented to reach self-compacting requirements in fresh concrete mixes, on the basis of evaluation of the flowing characteristics of these Mixtures. The compressive strength is most applicate characteristic of concrete. However, there are many defects for concrete materials, including low anti cracking performance, bad toughness, low tensile strength. During the failure of the concrete structure under the action of load, the energy consumed is very limited, and many cracks with different size scale will come into being. The utilization of concrete Incorporating with Fibers is one of the proper issues of construction industry in last years. The main focus of this research to design a high performance self-compacted Fiber Reinforced concrete by using an evolutionary algorithm, which is implemented in MATLAB software (R 22019 a) version. Crow Search Algorithm (CSA) and Genetic Algorithm (GA) are statistical ways which are developed by optimization based meta-heuristic solutions. A total of 67 concrete Mixtures were considered by varying the levels of key factors affecting concrete strength of concrete, namely, water content (137. 2-195 kg/m3), cement content (325. 5-520 kg/m3), coarse aggregate content (722-920 kg/m3), fine aggregate content (804. 9-960 kg/m3), nano silica content (0-49. 6 kg/m3), percentage of volumetric of Fibers (0-0. 9 %), lime stone powder content (0-288. 9 kg/m3) and superplasticizer content (1. 75-10. 5 kg/m3) were developed to design optimized Mixture proportions. The objective function called maximizing concrete strength was formulated as an optimization problem on the basis of Multiple Linear Regression (MLR) method. The constrains including ratio of Mixture proportions and absolute volume of Mixture design were utilized to obtain an optimal-strength and cost-effective design. The concrete technological constraints were identified as the factors of experimental design for concrete production. The evolutionary implementation of results reached incorporating Mixture proportions having strengths in range of 30-88. 7 MPa. Five numerical examples for optimum Mixture design of SCFRC were considered to evaluate the capability and efficiency of CSA and GA algorithm. These results were compared and concluded that CSA (3. 38-14. 49 % of mean error) performed better than GA (7. 95-15. 52 % of mean error) for this application. Also, the proposed evolutionary CSA and GA algorithms are found to be reliable and robustness tools to solve and optimize engineering and concrete technological problem.