  Efficient thermoelectric materials are highly desirable, and the quest for
finding them has intensified as they could be promising alternatives to fossil
energy sources. Here we present a general first-principles approach to predict,
in multicomponent systems, efficient thermoelectric compounds. The method
combines a robust evolutionary algorithm, a Pareto multiobjective optimization,
density functional theory and a Boltzmann semi-classical calculation of
thermoelectric efficiency. To test the performance and reliability of our
overall framework, we use the well-known system Bi$_2$Te$_3$-Sb$_2$Te$_3$.
