This study aims to optimize the manufacturing operations in the aerospace industry involving the cutting of regular and/or irregular (nesting problem) two dimensional shapes by addressing a multiple criteria such as material scrap as waste, storage constraints, customer due date considerations, material handling or tractability requirements and cutting machine setup time. The 2D cutting stock problem (2D-CSP) with only a scrap minimization criterion is a computationally challenging and a ‘strongly NP Hard’ problem. The additional requirements increase the complexity of the 2D-CSP because now a cutting plan or method has to satisfy potentially conflicting criteria which transforms the conventional single objective problem into a multiple-objective optimization problem. This study uniquely formulates and solves this multi-objective combinatorial optimization problem to yield a cutting plan that gives a compromise or trade-off for the competing multiple criteria. Non Dominated Sorting Genetic Algorithm (NSGA II), a state of the art solution algorithm, is proposed to solve this formulation giving a range of Pareto plans that will have an impact on the efficiency and material utilization for manufactures in the aerospace industry.