Realistic simulation of metal forming operations requires accurate material parameters. Identification of such parameters involves multiple mechanical tests under different conditions, giving rise to multiple experimental data sets. Within this context, the present work describes a strategy for identification of material parameters based on multi-objective optimisation. The Pareto Optimality Theory was adopted to approach the multi-objective problem, from which a global method formulated in a normalised feasible objective space is proposed. The normalised space is defined by using the ideal vector determined for all individual data set. The capacity of the method to solve non-convex problems with respect to both Pareto front and design variables is demonstrated by solving multi-objective problems defined by multivariate test functions. Two applications are discussed: (i) identification of temperature-dependent hardening parameters based on tensile tests under different thermal conditions and (ii) identification of hardening and damage parameters using tensile and compression tests.