The nonlinear methods of Least Squares and Maximum Likelihood estimation are the main methods in the theory of nonlinear regression analysis. The serious disadvantage of these methods is that they can be applied only when the distribution of errors is known or is similar to the normal distribution. Using the optimal design theory, we propose an original approach, which is independent of assumptions on errors distribution. This approach is applied to a parameter estimation problem in chemical equilibrium analysis.