Fuzzy measures is a measure for representing the membership degrees of an object to candidate sets. It is not easy to provide consistent fuzzy measure values with fuzzy measure properties since they have to be subjectively determined. Thus it induces an identification problem that determines measure values with fuzzy measure properties from human-provided measure values. The lambda-fuzzy measure is a typical fuzzy measure widely used. Several methods have been developed for lambda-fuzzy measure identification. Such methods, however, have restrictions on data set used in the identification, or require complicate computation, and thus not easy to use. Therefore, this paper proposes a lambda-fuzzy measure identification method based on genetic algorithms, and shows its applicability by some experiments.