In near future, robots are envisioned to work alongside humans in unstructured professional and domestic environments. In such setups, collaborative manipulation is a fundamental skill that allows manipulation of heavy loads by load sharing between agents. Grasp planning plays a pivotal role for load sharing but it has not received attention in the literature. This work proposes a grasp analysis approach for collaborative manipulation that allows load sharing by minimizing exerted grasp wrenches in a task specific way. The manipulation task is defined as expected external wrenches acting on the target object. The analysis approach is demonstrated in a two-agent decentralized set-up with unknown objects. After the first agent has grasped the target, the second agent observes the first agent's grasp location and plans its own grasp according to optimal load sharing. The method was verified in a human robot collaborative lifting task. Experiments with multiple objects show that the proposed method results in optimal load sharing despite limited information and partial observability.