This paper proposes a novel control methodology to enable accurate tracking of a path profile defined in output space. No temporal requirement is specified on this movement a priori, and the proposed framework enforces path tracking while minimizing an additional objective function. The problem is solved by formulating the problem as a constrained optimization involving simultaneous spatial tracking constraints and temporal via-point constraints. Practical implementation is via a two stage iterative learning control algorithm based on norm optimal and gradient updates which embeds robustness to plant uncertainty. The algorithm is verified using a gantry robot experimental platform, whose results reveal practical efficacy.