Automation of malware analysis is a complex challenge faced by researchers due to the growing number of unique malware samples. For this automation to succeed, the algorithm used in the scheduling decisions must be reliable and efficient to better use the available resources. In this paper we discuss our distributed approach in details, showing the reasoning behind each part composing the scheduler, the distribution of jobs and the analysis results.