In natural, properties of living organisms are complex, causing difficulties in the classification of biology related documents. Therefore this research utilizes genetic algorithms to aid in classification of biology document. On the classification of biology document, first we arrange known general database of biology related terms and then add terminology provided by experts in a specific field to create a general specialized terms database. However, a general specialized term database is insufficient for achieving the goal of auto-classification. Thus, we employ genetic algorithms to analyze and add more specific terms that can aid in the classification of biology documents, resulting in a detailed specialized terms database. For the fitness parameter, we consider a term’s frequency of appearance in the detailed specialized terms database versus a common terms database and the length of the term. This research used Council of Agriculture’s documents on edible and poisonous mushrooms as test data. After cross validation of the result, The accuracy of classification is 56% by only using general specialized term database, however, by using our approach can reach 78% accuracy. We believe that other types of documents can be classified based on the classification procedure presented.