This paper presents a new experimental framework for studying multi-label evolving stream classification, with efficient methods that combine the best practices in streaming scenarios with the best practices in multi-label classification. Many real world problems involve data which can be considered as multi-label data streams. Efficient methods exist for multi-label classification in non streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as the learners must be able to adapt to change using limited time and memory. We present a new experimental software that extends the MOA framework. Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. It is released under the GNU GPL license.