We study the problem of finding "fair" stable matchings in the Stable Marriage problem with Incomplete lists (SMI). In particular, we seek stable matchings that are optimal with respect to profile, which is a vector that indicates the number of agents who have their first-, second-, third-choice partner, etc. In a rank maximal stable matching, the maximum number of agents have their first-choice partner, and subject to this, the maximum number of agents have their second-choice partner, etc., whilst in a generous stable matching $M$, the minimum number of agents have their $d$th-choice partner, and subject to this, the minimum number of agents have their $(d-1)$th-choice partner, etc., where $d$ is the maximum rank of an agent's partner in $M$. Irving et al. [18] presented an $O(nm^2\log n)$ algorithm for finding a rank-maximal stable matching, which can be adapted easily to the generous stable matching case, where $n$ is the number of men / women and $m$ is the number of acceptable man-woman pairs. An $O(n^{0.5}m^{1.5})$ algorithm for the rank-maximal stable matching problem was later given by Feder [7]. However these approaches involve the use of weights that are in general exponential in $n$. In this paper we present an $O(nm^2\log n)$ algorithm for finding a rank-maximal stable matching using a vector-based approach that involves weights that are polynomially-bounded in $n$. We conduct an empirical evaluation, and show how this approach has a far reduced memory requirement (an estimated $100$-fold improvement for instances with $100, 000$ men or women) when compared to Irving et al.'s algorithm above. Additionally, we show how to adapt our algorithm for the generous case. Finally, we examine the complexity of the problem of finding profile-based optimal stable matchings in the Stable Roommates problem (SR).