Humanoid robots are expected to perform diverse manipulation tasks, yet this relies on a standing controller that is both robust and precise. Existing methods often fail to accurately control high-dimensional upper-body motions or to guarantee stability when these motions are fast. We propose a Time Optimization Policy (TOP) that enables humanoid robots to achieve balance, precision, and time efficiency simultaneously by optimizing the time trajectories of upper-body motions, rather than solely enhancing lower-body disturbance rejection. Our framework integrates three key components: (i) a variational autoencoder (VAE) to encode motion priors and improve coordination between upper and lower body, (ii) a decoupled controller consisting of an upper-body PD controller for precision and a lower-body RL controller for robustness, and (iii) joint training of the controller with TOP to mitigate destabilization caused by fast upper-body motions. We validate our approach in both simulation and real-world experiments, demonstrating superior stability and accuracy in humanoid standing manipulation tasks.