The computation of parameters for group contribution models in order to predict thermodynamic properties usually leads to a multiparameter optimization problem. The model parameters are calculated using a regression method and applying certain error criteria. A complex objective function occurs for which an optimization algorithm has to find the global minimum. For simple increment or group contribution models it is often sufficient to use simplex or gradient algorithms. However, if the model contains complex terms such as sums of exponential expressions, the search of the global or even of an fairly good optimum becomes rather difficult. Evolutionary Algorithms represent a possibility for solving such problems. In most cases, the use of biological principles for optimization problems yields satisfactory results. A genetic algorithm and an optimization method using an evolutionary strategy were programmed at the Institute for Thermodynamics at the University of Dortmund and were tested with an Enthalpy Based Group Contribution Model (EBGCM). The results obtained with these procedures were compared with the results obtained using a simplex algorithm. A test system was created and the corresponding objective function was examined in detail. For this purpose, 3D-plots were produced by varying two out of six model parameters. In this paper, the development of a genetic algorithm is presented and the fitting procedure of the model parameters is discussed. Part 2 of this article series will deal with the efficiency of evolutionary strategies applied to such a prototype of non-linear regression problems. (C) 1998 Published by Elsevier Science Ltd. All rights reserved.