Design solutions for the problems of the rock side slope stability, in general, are one of the most critical issues in geo-technical engineering profession, due to the fact that the values of rock parameters are difficult to be considered reliable, because of the uncertainties existing in geological system. This research aims to introduce reliability based design of the rock side slope stability considering optimal cost value. The idea for obtaining the optimal cost value depends on two main critical parameters. These two main critical parameters are uni-axial compressive strength (U.C.S) and geologic strength index (G.S.I). Both of them have been calculated based on statistical calculations of mean and standard deviations. A strength criterion is needed to characterize the rockmass in geotechnical engineering. There are three criteria are used to describe the strength of a material: bilinear Mohr-Coulomb criterion, nonlinear Hoek-Brown criterion and special spalling criterion must be used in special case of massive, brittle rocks. Nonlinear Hoek-Brown criterion has been used in this study; because of some limitations associated with the use of the Mohr-Coulomb criterion. RocData program have been utilized for obtaining the Hoek-Brown parameters (m, s), Slide program has been employed for calculating the reliability index, factor of safety and probability of failure for each case study for upper plateau in Mokattam area. The concept of design is based on the necessity of using reinforcement. If the design of using rock parameters is safe and reliable, there is no need for reinforcement; otherwise design of rock bolts should be used as a support system to improve reliability and factor of safety. The current simplified method has been utilized to consider the cost of failure to find the optimal length of support and optimal reliability index. The study concluded that the suggested technique of nonlinear Hoek Brown Criterion is effective for helping the designer in finding the optimal length of support that meets the optimal reliability index and optimal cost value for two main critical parameters.