We study welfare maximization under envy-free contracts, where a principal assigns tasks and offers success-contingent payments to heterogeneous agents with hidden effort. For unit-demand agents, we establish a sharp complexity boundary: the problem is polynomial-time solvable when each agent is interested in at most two tasks, but NP-hard with at most three interests. Our algorithm analyzes tree-structured dependencies among forced assignments and reduces optimization to maximum-weight independent set in a perfect graph. When agents may receive multiple tasks, no polynomial-time constant-factor approximation exists unless $\mathrm{P}=\mathrm{NP}$. Finally, with homogeneous success probabilities and rewards, we establish a welfare- and revenue-preserving equivalence between unit-demand contract design and envy-free pricing. This yields polynomial-time welfare maximization without restricting interests, while revenue maximization remains NP-hard.

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