A multi - objective evolutionary algorith m (MOEA) approach is pr esented in this paper. The algorithm (DFBMOEA) aims to improve convergence of Pareto - based MOEAs to the true Pareto optimal set/Pareto front and remove decision maker interaction from the process . A novel distance function is used as a fitness function for MOEA. A range equalisation function and a reference vector are utilised to eliminate the prior knowledge required from decision makers. A 0 & 1 knapsack problem [27] was tested to demonstrate the performance of our appro ach compared to two leading MOEAs : Non - dominated Sorting Genetic Algorithm II (NSGA II) and Strength Pareto EA II (SPEA II). The results show that our approach produced a set of effective Pareto optimal solutions that are comparable to th e two leading MOEA s.