In recent years, multi-objective evolutionary algorithms (MOEA) have generated a large research interest. MOEA's attraction stems from their ability to find a set of Pareto solutions rather than any single, aggregated optimal solution for a multi-objective! problem. As for single-objective evolutionary algorithms (SOEA), multi-objective evolutionary algorithms also require parameter tuning to achieve desirable performance. In the literature we can find Fuzzy Logic Controllers (FLC's) applied to online parameter control for SOEA. In this paper, we propose to use a FLC to dynamically adjust the parameters of a particular Multi-Objective Differential Evolution (MODE) algorithm. The fuzzy logic controlled multi-objective differential evolution (FLC-MODE) is applied to a suite of benchmark functions. Its results are compared to those obtained by using MODE with constant parameter settings. We show that the FLC-MODE obtains better results in 80% of the testing examples. Given that the benchmarks were synthetic test functions, we designed the FLC using only our understanding of the working mechanism of the MODE, without incorporating any additional problem-specific knowledge. When addressing real-world applications, we expect the FLC to be an excellent way for representing and leveraging their associated heuristic knowledge.