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Agents operating in a multi-agent environment must consider not just their actions, but also those of the other agents in the system. Artificial social systems are a well-known means for coordinating a set of agents, without requiring centralized planning or online negotiation between agents. Artificial social systems enact a social law which restricts the agents from performing some actions under some circumstances. A robust social law prevents the agents from interfering with each other, but does not prevent them from achieving their goals. Previous work has addressed how to check if a given social law, formulated in a variant of ma-strips, is robust, via compilation to planning. However, the social law was manually specified. In this paper, we address the problem of automatically synthesizing a robust social law for a given multi-agent environment. We treat the problem of social law synthesis as a search through the space of possible social laws, relying on the robustness verification procedure as a goal test. We also show how to exploit additional information produced by the robustness verification procedure to guide the search.