The paper analyses the issues behind strategies optimization of an existing automated warehouse for the steelmaking industry. Genetic Algorithms are employed to this purpose by deriving a custom chromosome structure as well as ad-hoc crossover and mutation operators. A comparison between three different solutions able to deal with multiobjective optimization are presented: the first approach is based on a common linear weighting function that combines different objectives; in the second, a fuzzy system is used to aggregate objective functions, while in the last the Strength Pareto Genetic Algorithm is applied in order to exploit a real multiobjective optimization. These three approaches are described and results are presented in order to highlight benefits and pitfalls of each technique.