The computation of parameters of group contribution models in order to predict thermodynamic properties usually leads to a multiparameter optimization problem where the model parameters are calculated using a regression method. A complex objective function occurs for which an optimization algorithm has to find the global minimum. Simple increment or simple group contribution models often result in unimodal regression problems, for which deterministically acting algorithms are suitable. If the model contains parameters in complex terms such as sums of exponential expressions, the optimization problem will be a nonlinear regression problem which often results in a multimodal optimization problem. In this case the search of the global or at least a fairly good optimum becomes rather difficult. Evolutionary algorithms are suitable for solving such multimodal problems. Friese, T., Ulbig, P., & Schulz, S. (1998). Use of evolutionary algorithms for the calculation of group contribution parameters in order to predict thermodynamic properties (Part 1): Genetic algorithms. Computers and Chemical Engineering 22(11), 1559-1572 showed that the efficiency of genetic algorithms applied to the presented optimization problem, this paper shows that evolution strategies are suitable, as well. This work first describes the typical mode of acting of evolution strategies before a new variant, the so-called encapsulated evolution strategy using a multidimensional step-length control is introduced. This new type of strategy proved to be superior to conventional evolution strategies and genetic algorithms. In order to benefit from this new algorithm for other similar optimization problems, an optimum strategy type is determined and analyzed with the help of two visualized test systems representing the complex of optimization problems, which nonlinear parameter fittings of group contribution model parameters belong to.