This paper presents an efficient algorithm with performance guarantee to solve task scheduling problem on hybrid platforms with energy constraint and communication delays. The underlying platform architecture in this work is composed of two types of resources, CPU and GPU, often called hybrid parallel multicore platforms. We focus on finding a generic approach to schedule applications presented by Directed Acyclic Graph (DAG), which minimizes the makespan by considering communication delays and respecting an energy constraint. A two‐phase algorithm is proposed with a performance guarantee of 6 compared with the optimal solution; the first phase consists in solving the assignment problem to find the type of processor assigned to execute the tasks (CPU or GPU) using a linear program. In the second phase, we calculate the start execution time of each task to generate a feasible schedule. Finally, we test our algorithm on a large number of instances. These tests demonstrate that the proposed algorithm achieves a close‐to‐optimal performance.