This  paper  focuses  on  the  development  of  an  efficient  distributed  collaborative optimization  method  for  the  design  of  remote  sensing  small  satellite  mission  in  low  earth  orbit (LEO). The satellite mission requirement involves the duration in which the satellite is able to take images, send data to the ground station and the amount of information it can store. Conventionally, all at  once  methods  are  used  in  satellite  mission  analysis,  however,  design  optimization  of  such systems are multidisciplinary task  with multiple conflicting objectives such as cost, performance and  reliability.  The  approach  adopted  in  this  paper  is  based  on  a  distributed  collaborative optimization (CO)  framework.  In this  approach,  the  design  optimization  problem  is  divided  into two  levels;  namely  system  and  discipline  levels.  The  discipline  level  optimization  involves payload, power, mission and launch subsystems. The objective  function of the system level is to minimize the resolution of the satellite imaging payload subject to equality constraints. The use of equality  constraints  at  the  system  level  in  CO  to  represent  the  disciplinary  feasible  regions introduces numerical and computational difficulties, as the discipline level optima are non-smooth and noisy  functions of the system level optimization parameters. As a result of these difficulties, derivative-based  optimization  techniques  cannot  be  used  for  the  system  level  optimization.  To address these difficulties a robust optimization algorithm, genetic algorithms (GA), are used at the system  level,  whilst  at  the  discipline  level  efficient  gradient  based  techniques  are  utilized.  The results show that distributed CO framework using GA has the same level of accuracy as with the conventional  all  at  once  approaches,  while providing  a  potential  approach  for  solving  complex multidisciplinary design problems such as the design of satellite systems.