<p>The low-rank matrix optimization with affine manifolds (rank-MOA) aims to minimize a continuously differentiable function over a low-rank set intersecting with an affine manifold. In this talk, we will give the optimality analysis for rank-MOA. As a cornerstone, the intersection rule of the Fr\acte{e}chet normal cone to the feasible set of rank-MOA is established under some mild linear independence assumptions. Aided with the resulting explicit formulae of the underlying normal cones, the so-called F-stationary point and the \alpha-stationary point of rank-MOA are investigated and the relationship with local/global minimizers are then revealed in terms of first-order optimality conditions. Furthermore, the second-order optimality analysis, including the necessary and the sufficient conditions, is proposed based on the second-order differentiation information of the model. All these results will enrich the theory of low-rank matrix optimization and give potential clues to designing efficient numerical algorithms for seeking low rank solutions. Meanwhile, two specific applications of rank-MOA are discussed to illustrate our proposed optimality analysis.</p>