We analyze Shannon information of scale-free networks in terms of their assortativeness, and identify classes of networks according to the dependency of the joint remaining degree distribution on the assortativeness. We conjecture that these classes comprise minimalistic and maximalistic networks in terms of Shannon information. For the studied classes, the information is shown to depend non-linearly on the absolute value of the assortativeness, with the dominant term of the relationship being a power-law. We exemplify this dependency using a range of real-world networks. Optimization of scale-free networks according to information they contain depends on the landscape of parameters' search-space, and we identify two regions of interest: a slope region and a stability region. In the slope region, there is more freedom to generate and evaluate candidate networks since the information content can be changed easily by modifying only the assortativeness, while even a small change in the power-law's scaling exponent brings a reward in a higher rate of information change. This feature may explain why the exponents of real-world scale-free networks are within a certain range, defined by the slope and stability regions.