Hierarchical Multi-Label Classification is a complex classification problem where an instance can be assigned to more than one class simultaneously, and these classes are hierarchically organized with superclasses and subclasses, i.e., an instance can be classified as belonging to more than one path in a hierarchical structure. We investigate the use of a neural network method and a genetic algorithm in this classification task, and compared their performances with other methods in the literature.