Robots are increasingly pervasive in manufacturing. However, robotic grippers are often still very simple parallel-jaw grippers with flat fingers, which are very sub-optimal for many objects. Having engineers design a new gripper for every object is a very expensive and inefficient process. We instead propose to automatically design them using machine learning. First, we use Evolutionary Strategies in simulation to get a good initial gripper. We also propose an automatic curriculum design that automatically increases the difficulty of the design task in simulation to ease the design process. Once the gripper is designed in simulation we fine-tune it via back-propagation on a Graph Neural Network model trained on real data for many grippers and objects. By amortizing real-world data across grippers and objects we can be very data-efficient in the real world, leveraging prior experience in a manner analogous to that of meta-learning. We show that our method improves the default gripper by significant margins on multiple datasets of varied objects.