This paper aims to address the nonlinear optimal guidance problem with impact-time and impact-angle constraints, which is fundamentally important for multiple pursuers to collaboratively achieve a target. Addressing such a guidance problem is equivalent to solving a nonlinear minimum-effort control problem in real time. To this end, the Pontryagain's maximum principle is employed to convert extremal trajectories as the solutions of a parameterized differential system. The geometric property for the solution of the parameterized system is analyzed, leading to an additional optimality condition. By incorporating this optimality condition and the usual disconjugacy condition into the parameterized system, the dataset for optimal trajectories can be generated by propagating the parameterized system without using any optimization methods. In addition, a scaling invariance property is found for the solutions of the parameterized system. As a consequence of this scaling invariance property, a simple feedforward neural network trained by the solution of the parameterized system, selected at any fixed time, can be used to generate the nonlinear optimal guidance within milliseconds. Finally, numerical examples are presented, showing that the nonlinear optimal guidance command generated by the trained network can not only ensure the expected impact angle and impact time are precisely met but also requires less control effort compared with existing guidance methods.