The existing Improved Multi-label Lazy Learning Approach (IMLLA) has the problem that the influence of similarity information is ignored with only the neighbor label correlation information considered when the neighbor labels were used, which may reduce the robustness of the approach. To solve this problem, with firefly method introduced and the combination of similarity information with label information, a Multi-label Lazy Learning Approach based on FireFly method (FF-MLLA) was proposed. Firstly, Minkowski distance was used to measure the similarity between samples to find the neighbor point. Secondly, the label count vector was improved by combining the neighbor point and firefly method. Finally, Singular Value Decomposition (SVD) and kernel Extreme Learning Machine (ELM) were used to realize linear classification. The robustness of the approach was improved due to considering both label information and similarity information. The experimental results demonstrate that the proposed approach improves the classification performance to a great extent compared to other multi-label learning approaches. And the statistical hypothesis testing and stability analysis are used to further illustrate the rationality and effectiveness of the proposed approach.