This paper focuses upon the development of an efficient method for the conceptual design optimization of Remote Sensing Satellites (RSS) under uncertainty. There are many acceptable optimal solutions for implementation of satellite subsystems in a space system mission. Every solution should be assessed based on the different criteria such as cost, mass, reliability and payload resolution. In the present paper satellite mass and imaging payload resolution were considered as system level objective functions to obtain the system optimal solution during the conceptual design phase. Furthermore, two Multidisciplinary Design Optimization (MDO) frameworks; Multidisciplinary Design Feasible (MDF) and distributed Collaborative Optimization (CO) were applied to the multi-objective design optimization problem under uncertainty. Also, various uncertainties involving environment, operation, geometry, subsystems, etc. were considered in the Reliability Based Multidisciplinary Design Optimization (RBMDO) frameworks. In the present study, MDF, CO, Reliability Based Multi-disciplinary Design Feasible (RB-MDF) and Reliability Based Collaborative Optimization (RB-CO) frameworks were evaluated and compared. The methodology was based on the utilization of Monte Carlo simulation method for accounting uncertainties in design process and applying genetic algorithms and sequential quadratic programming to system level and discipline level optimizers. Results obtained in this study, have shown that the introduced method provides an effective way of accounting uncertainty in a complex space system design such as the conceptual design optimization of a spacecraft.