This paper suggests the opportunity of taking advantage of genetic techniques in order to approach Portfolio Selection following conceptual models that handle risk in a non-trivial way. We investigate the possibility of applying genetic techniques to this problem. It turns out that Portfolio Selection lends itself to a distributed genetic implementation. Finally we illustrate an implementation of some of the ideas presented in this paper and give some preliminary experimental results supporting the feasibility and the advantages of the proposed approach.