The purpose of this paper is to introduce various types of optimality criteria used in parameter estimation. The main idea of ​​such criteria is to improve statistical inferences about the parameters and quantities of interest by appropriately choosing the values ​​of the control (predictor) variables. Depending on the different quantities we wish to infer, different criteria have been introduced in this field. In this paper, in addition to examining the criteria for linear models and presenting a numerical algorithm, we also consider their generalization for the more general case, which is the nonlinear model. Since in the nonlinear case, the criteria depend on unknown parameters, we will discuss different approaches to solve this problem.