Fuzzy genetics-based machine learning is one of data mining techniques based on evolutionary computation. It can generate accurate classifiers with a small number of fuzzy if-then rules from numerical data. Its multiobjective version can provide a number of classifiers with a different tradeoff between accuracy and complexity. One major drawback of this method is the computation time when we use it for large data sets. In our previous study, we proposed parallel distributed implementation of single-objective fuzzy genetics-based machine learning which could drastically reduce the computation time. In this paper, we apply our idea of parallel distributed implementation to multiobjective fuzzy genetics-based machine learning. Through computational experiments on large data sets, we examine the effects of parallel distributed implementation on the search performance of our multiobjective fuzzy genetics-based machine learning and its computation time.