An innovative numerical algorithm for solving infinite-horizon optimal control problems is introduced in this paper, using the IsoCost-HyperSurface (ICHS) concept. In the state space of an optimal control system, an ICHS is defined as a set of state points having a certain amount of the cost value. In this paper, it is proved that for a certain cost amount, the ICHS resulting from the infinite-horizon optimal control solution, surrounds all other ICHSs resulting from non-optimal control strategies. Regarding this geometric feature, the novel Isocost Dynamic Programming (IDP) algorithm is introduced to search for optimal control solutions. As an illustration of the introduced ICHS concepts and to demonstrate the effectiveness of the proposed IDP algorithm, several simulated examples are presented. The results are compared with those of conventional DP. These comparisons demonstrate that the proposed algorithm has better relative optimality compared to the DP algorithm with 18% lower cumulative cost value, by comparing results from the closed-loop control of two nonlinear systems with random initial conditions. More significantly, when compared to the DP algorithm, the IDP was able to enhance computational performance by reducing the execution time by 21 % while using less memory.