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The field of Differential Evolution (DE) has demonstrated important advantages in single objective optimization. To date, no previous research has explored how the unique characteristics of DE can be applied to multi-objective optimization. This paper explains and demonstrates how DE can provide advantages in multi-objective optimization using directional information. We present three novel DE variants for multi-objective optimization, and a report of their performance on four multi-objective problems with different characteristics. The DE variants are compared with the NSGA-II (Nondominated Sorting Genetic Algorithm). The results suggest that directional information yields improvements in convergence speed and spread of solutions.