Design of experiments (DOE) for parameter estimation in dynamic systems is receiving more attention from process system engineers. In this paper, a principal component analysis (PCA)-based optimal criterion (P-optimal) for model-based DOE is proposed that combines PCA with information matrix analysis. The P-optimal criterion is a general form that encompasses most widely used optimal design criteria such as D-, E-, and SV-optimal, and it can automatically choose the optimal objective function (criterion) to use for a specific differential and algebraic (DAE) system. Two engineering examples are used to validate the algorithms and assumptions. The advantages of P- optimal DOE include case of reducing the scale of the optimization process by choosing parameter subsets to increase estimation accuracy of specific parameters and avoid an ill-conditioned information matrix.