We propose a new data structure for the efficient computation of the nondominance problem which occurs in most multi-objective optimization algorithms. The strength of our data structure is illustrated by a comparison both to the linear list approach and the quad tree approach on a category of problems. The computational results indicate that our method is particularly advantageous in the case where the proportion of the nondominated vectors versus the total set of criterion vectors is not too large.