  A method is proposed to identify target states that optimize a metastability
index amongst a set of trial states and use these target states as milestones
(or core sets) to build Markov State Models (MSMs). If the optimized
metastability index is small, this automatically guarantees the accuracy of the
MSM, in the sense that the transitions between the target milestones is indeed
approximately Markovian. The method is simple to implement and use, it does not
require that the dynamics on the trial milestones be Markovian, and it also
offers the possibility to partition the system's state-space by assigning every
trial milestone to the target milestones it is most likely to visit next and to
identify transition state regions. Here the method is tested on the Gly-Ala-Gly
peptide, where it shown to correctly identify the expected metastable states in
the dihedral angle space of the molecule without \textit{a~priori} information
about these states. It is also applied to analyze the folding landscape of the
Beta3s mini-protein, where it is shown to identify the folded basin as a
connecting hub between an helix-rich region, which is entropically stabilized,
and a beta-rich region, which is energetically stabilized and acts as a kinetic
trap.
