The evolution of the Intelligent Transportation System (ITS) has driven dramatic increase in data exchange on road. Sub-6 GHz assisted millimeter wave (mmWave) technology is a promising solution to address this challenge. The sub-6 GHz channel serves as the control plane to facilitate peer discovery of mmWave equipped vehicles, while the mmWave technology provides high throughput data transmission. Considering the huge amounts of data and the conflicts among mmWave links, a sub-6 GHz assisted mmWave scheduler is indispensable. In this article, we study the mmWave scheduling problem that captures Vehicle-to-Everything (V2X) communication features, i.e., data importance and data freshness. We formulate the scheduling problem into a novel mixed-integer sum-of-ratios problem by leveraging discretized mmWave transmission slots. For this nonlinear NP-hard problem, we derive an equivalent mixed-integer linear programming (MILP) problem with significantly reduced computational complexity. Extensive comparative simulation studies with the baseline brute-force algorithm, the greedy coloring algorithm, our previously developed Maximum Weight Matching-based Iterative Algorithm (MWMIA) and Parameterization-based Iterative Algorithm (PIA) are conducted to verify the correctness and effectiveness of our proposed algorithm.