The RoboCup Soccer Simulation 2D league uses autonomous agents to compete in a simulated soccer environment. The agent uses action generators and evaluator functions to create a pool of actions and select the best action. However, a complex evaluation function increases computational demands when evaluating multiple passes, impacting real-time decision-making during the game. We propose a machine learning architecture for single-pass generation for the Soccer Simulation 2D environment, independent of position bias and player role. Our model performs a single-step target point generation for passing, avoiding multiple inferences of a costly evaluation function, making it up to 3.07 times faster than the current approach. The effectiveness of the Learnable Field Evaluator model is measured by a Root Mean Square Error (RMSE) of 309.2768, Kendall- $\tau$ of 0.9902, and Spearman- $\rho$ of ${0. 9 9 9 8}$. Also, the pass generated by the Target Point Generator is selected in ${9 9. 9 8 \%}$ of the cases when compared to the passes generated by the heuristic pass generator. In conclusion, SPO learns the heuristic field evaluation function and how to generate optimized passes.