Multi-objective optimization is a widely applicable problem in Engineering and Computer Science. In the past, Cultural Algorithms have been used to solve complex optimization and design problems. In this thesis we extend the Cultural Algorithm Framework to support multi-objective problems. The resultant system, Multi-Objective Cultural Algorithms (MOCA), can be used independently or as a supplement to existing MO optimization methods. We compare the performance of our algorithm with NSGA-II using problems from the DTLZ test suite, a popular MOEA test suite. We found that Cultural Algorithms are a promising technique for solving multi-objective problems.