The need for first principles based models for chemical and biological processes has led to the development of techniques for model-based design of experiments (MBDOE). These techniques help in speeding up the parameter estimation efforts and typically lead to improved parameter precision with a relatively short experimental campaign. In the case of complex kinetic networks involving parallel and/or consecutive reactions, correlation among model parameters makes the inverse problem of parameter estimation very difficult. It is therefore important to develop experimental design techniques that not only increase information content about the system to facilitate precise parameter estimation but also reduce the correlation among parameters. This article presents a multiobjective optimization (MOO) based framework for experimental design, where, in addition to the traditional objective of eliciting maximally informative data for parameter estimation, an explicit objective to reduce correlation among parameters is included. The proposed MOO based framework is tested on two case studies, and results are compared with the traditional alphabetical designs. The approach provides a pictorial representation of trade-off between system information and correlation among parameters in the form of Pareto-optimal front, which offers the experimentalist the freedom to choose experimental design(s) that are most suitable to implement on the experimental system and realize the benefits of such experiments.