Abstract: Workflow scheduling is solved using heuristics and meta-heuristics. Heuristics are used to get an optimal solution while meta-heuristics are used to get near optimal solution. Meta-heuristics are general purpose method of solving different types of problem. This study puts Double Hybrid Multi-objective Non-Dominated Sorting Genetic Algorithm (DHNSGA-II) that gears up the convergence of the algorithm. DHNSGA-II does hybridization at two levels. At the first level it uses pre-selection operator. At the second level it uses Memetic algorithm. Pre-selection operator seeds the DHNSGA-II with the previously generated solutions. Memetic algorithm improves the current population using multi-objective local search. Apart from DHSNGA-II researchers introduced an approach to rank the Pareto frontiers because Pareto frontier has many solutions; it is nearly impossible to choose the best solution. The experimental result reveals that the proposed approach in this research performs well in optimizing the workflow scheduling jobs.