Parameter estimation is a key concern for reliable and predictive models of biological systems. In this paper, we propose a multi-objective, multi-state optimization framework that allows multiple data sources to be incorporated into the parameter estimation process. This enables the model to better represent a diverse range of data from both within and outwith the training set; and to determine more biologically relevant parameter values for the model parameters. The framework is based on a multi-objective PSwarm implementation (MoPSwarm) and is validated via a case study on the ERK signalling pathway, in which significant advantages over the conventional single-state approach are demonstrated. Several variants of the framework are analyzed to determine the optimal configuration for convergence and solution quality.