<p>The rapidly expanding global market for metal additive manufacturing suggests significant opportunities for energy and material conservation. Despite its significance, limited number of studies have systematically investigated effect of input process parameter on resource utilization efficiency in the metal additive manufacturing process. The present study aims to bridge this gap, through process parameter optimization, with emphasis on dimensional conformance, resource utilization efficiency and productivity. Resource utilization efficiency was assessed in terms of potential material wastage and net energy consumption. Furthermore, a novel quantitative tool has been developed to estimate the material wastage associated with geometric irregularities in WAAM built parts. The experiments have been carried out as per design of experiment by adopting the response surface methodology to identify the relationship among critical design variables. Finally, the analysis of variance has been employed to test the significance and contribution of input process parameters. Results presented that the wire feed speed was the most significant parameter followed by torch travel speed, coolant temperature and arc correction. A multi-objective optimization framework employing the Non-Dominated Sorting Genetic Algorithm-(NSGA-II) has been implemented to simultaneously handle conflicting response variables. Furthermore, confirmation experiments were performed to validate the optimization results. A good agreement between predicted and experimental values was observed as estimated variance was well within predicted interval derived from ANOVA error variance.</p>